Study of Human Life Sign Detection under
Debris or behind Barrier using Multi Frequency
Radar System
Submission of Final Report
of
Major Research Project
to
UGC, New Delhi
Project file
and Date
: MRP F.No.43-306/2014 (SR), 05 Sep. 2015
Tenure of
Project : 01/07/2015 to 30/06/2018
Grant
Sanctioned : 8.59 Lakhs
Name of
Investigator : Prof. Dr. Abhay N Gaikwad
Name of Co-
Investigator : Assoc.Prof K S Dongre
Department : Electronics and Telecommunication
Engineering Name of the
Institute : Janata Shikshan Prasarak Mandal‟s Babasaheb
Naik College of Engineering Pusad, 445215,
Dist. Yavatmal, Maharashtra, India.
Declaration
We hereby certify that the work which is being presented in this research project report
titled Study of Human Life Sign Detection under Debris or behind Barrier using Multi
Frequency Radar System submitted to UGC New Delhi under MRP scheme is an
authentic record of our own work carried out during the period 01/07/2015 to
30/06/2018.
Prof Kalpesh S Dongre Prof. Dr. A.N.Gaikwad
Co-Investigator Principal Investigator
This is to certify that the above statement made by investigators is correct to the
best of our knowledge.
Principal
Acknowledgements
We take this opportunity to thanks UGC, New Delhi, Govt. of India, for financial
support under Major Research Project (MRP F. No. 43-306/2014 (SR) dated 05 Sept.
2015.
We also owe our sincere gratitude to Head of the Department, Electronics and
Telecommunication, Dr. N P Jawarkar, Principal Dr. H B Nanvala of our Institute,
Babasaheb Naik College of Engineering Pusad and the Management of Janata Shikshan
Prasarak Mandal, Pusad for encouraging and providing the necessary facilities. We
would like to acknowledge the efforts of students of final year project batches for their
valuable assistance in data collection. Thanks are also extended to Mr. Utkarsh
Verulkar, PG student for assisting in work in initial stages of project. We thanks all the
committee members related to the project for there support and giving valauable inputs
for the improvements in the work.
Last but not least, we would like to thanks all the faculty members, supporting staff,
friends and our family members for their support.
Abstract
In recent years, there is need of new research techniques directed towards developments
of systems for searching and rescuing human victims buried under piles of rubble due
to natural disasters or manmade disasters. Existing methods for rescue operations are
utilizations of dogs, or seismic or optical devices which are not effective if the rubble or
debris covering the human victims is thicker than a few feet, especially for the case
when the victims are completely trapped or too weak to respond to the signal sent by
the rescuers. Nowadays, microwave based radar sensor which allows seeing through
visually opaque material is attracting attention of researchers. It has numerous civilian,
law enforcement and military applications. Microwave based sensor usage can be
extended in search and rescue operations to detect buried people after disasters.
The radar techniques often employed are impulse radar, frequency modulation
continuous wave (FMCW), step frequency continuous wave (SFCW) and ultra wide
band (UWB) noise radar for detection of hidden targets. SFCW in UWB range
possesses several advantages over impulse radar systems. The challenging aspect of
radar is to produce high quality images. For high quality, high resolution and low
attenuation of signal is required. Both requirements are contradictory to each other thus
there is tradeoff between resolution and attenuation. UWB based SFCW radar not only
allows detection of closely positioned targets, but also provides the target information
about shape, position and material content. The other advantage of UWB based SFCW
radar over impulse radar is greater measurement accuracy because it is much easier to
synthesize a pure tone at a frequency than to measure a time delay. SFCW has greater
dynamic range and lower noise because it can transmit at higher power and uses a very
narrow IF bandwidth. High average transmitting power can be obtained due to use of
continuous wave signal which helps in detecting trapped victims in longer vicinity.
Researchers are analyzing factors influencing life sign detection, different algorithms
for detection and discriminations and developing prototype. Both heartbeat and
breathing motions may cause changes in frequency, phase, amplitude and arrival time
of returned signal from a living human body. These responses however may attenuate
drastically due to the thickness and the electrical properties of the obstructions.
Therefore getting successful radar based detection of life sign is usually considered to
be a challenging task.
The problems posed for detection with search and rescue sensor system are strong
interfering signal due to radiation of radar transmitter and reflections from obstructing
medium and interference signals due to multiple reflections from other objects.
Obstructing medium like rubble can be a very attenuating medium, particularly if
metallic grids are embedded. Furthermore as rubble is very inhomogeneous medium,
local discontinuities can act as back scatterers and therefore can irradiate the operator,
thus preventing the detector from being able to distinguish between the operators signal
and that of the survivor. For these reasons, the application of microwave transceivers in
detecting human beings trapped under rubble has been disappointing.
Thus there is great need for developing a new sensitive life detection system which can
be used to locate human victims trapped deep under earthquake rubble or collapsed
building debris. Especially the system needs to be sensitive enough to detect breathing
signal of stationary victims who are completely trapped or too weak to respond to the
existing detection system. Considerable amount of attention and research is required
for dealing problems of search and rescue sensor systems.
The objective of this research project is detection of human life signs through visually
opaque materials like different types of walls. The detection of life sign becomes more
challenging when no apriori information of walls and human targets is available. The
scene may consist of various types of targets with different shapes and material
properties (dielectric) along with human being. Thus the objectives are to detect, locate
and extract the life sign signals which will be useful to the end user for interpretation.
To extract life sign, various signal processing techniques are used. Comparison of
signal processing techniques which are used to obtain breathing frequency of a human
target hiding behind a wall is addressed in chapter 3. Two different signal processing
techniques Fast Fourier transform (FFT) and Hilbert Huang transform (HHT) have been
applied on experimental data. After obtaining location of breathing signal using
standard deviation (SD), breathing frequency is obtained by FFT and HHT methods.
The values of breathing frequencies obtained in the results are in acceptable range.
In chapter 4, instead of reflection parameter, transmission parameter is measured using
single antenna i.e., monostatic radar system with the help of circulator. The
experimental results shows that proposed system is useful when the distance between
radar and target is increased compared to single antenna system without circulator.
Since the human target is same for data collected when plywood wall and brick wall is
used, the respiration frequency values remains same as observed from results.
Detection of human being life signs from behind the wall is addressed in chapter 5
using step frequency continuous wave (SFCW) based radar system. The major problem
in life sign detection is the reflection due to wall which amounts for substantial loss of
energy. The remaining energy signal passes through wall and propagated towards
human being as target. Finally the weak reflected signal from target reach to the
receiving antenna after passing through wall again. To improve the signal strength of
the target and hence detection, clutter reduction technique is proposed. It is observed
that after application of clutter reduction technique, the microwave radar system can
detect human life sign. The performance of Singular Value Decomposition (SVD)
based clutter reduction technique is compared with moving average clutter reduction
technique. It is observed that SVD technique outperforms moving average technique in
removing clutter. After improving signal to clutter ratio, location of life sign signal i.e.,
breathing signal is obtained using standard deviation and frequency of breathing signal
is extracted using Fast Fourier transform (FFT) successfully.
Chapter 6 demonstrates the extraction of human breathing frequency when the human
is standing behind brick wall of thickness 32 cm and 22 cm. Data were collected by
changing distance between antenna and wall, as well as distance between wall and
target was varied . It is observed from results, as the thickness of wall increases,
distance between antennas and target increases, detection of target peak becomes
difficult due to very low amplitude value. But still breathing frequency was extracted
successfully after processing the data. Also it is observed from the results that the
number of reflections increases due to multi paths when the distance between antenna
and target increases.
Chapter 7 presents the summary of contributions made in the research project and
future scope of work.
Contents
Contents......................... ................................................................................................... xi
List of Figures.................................................................................................................. xv
List of Tables ................................................................................................................ xvii
Nomenclature ................................................................................................................. xix
Chapter 1 Introduction ................................................................................................. 1
1.1 Motivation .......................................................................................................... 1
1.2 Basic and Operating Principle of SFCW Radar................................................. 4
1.3 SFCW Radar Parameters ................................................................................... 6
Chapter 2 Literature Survey ....................................................................................... 13
2.1 Introduction ...................................................................................................... 13
2.2 Review of work related to type of Radar used................................................. 13
2.3 Review of work related to clutter reduction .................................................... 14
2.4 Review of work related to Signal Processing .................................................. 15
Chapter 3 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques ................................................................... 21
3.1 Introduction ...................................................................................................... 21
3.2 Methodology .................................................................................................... 21
3.2.1 Experimental setup ................................................................................... 22
3.2.2 Signal Processing...................................................................................... 23
3.3 Result ............................................................................................................... 27
3.3.1 Processing for presence of target behind wall .......................................... 27
3.3.2 Stacking all 1024 traces ............................................................................ 28
3.3.3 Location determination using standard deviation..................................... 28
3.3.4 FFT based results ...................................................................................... 28
3.3.5 HHT based results .................................................................................... 30
3.4 Conclusion ....................................................................................................... 32
Chapter 4 Detection of location and breathing signal of human standing behind
brick wall using monostatic radar system ....................................................................... 33
4.1 Introduction ...................................................................................................... 33
4.2 Methodology ................................................................................................... 34
4.2.1 Development of Experimental Setup ....................................................... 35
4.2.2 Data collection ......................................................................................... 41
4.2.3 Signal Processing ..................................................................................... 43
4.3 Result and Discussion ..................................................................................... 46
4.3.1 Metal Calibration ..................................................................................... 46
4.3.2 Determination of location for data taken in corridor with plywood ........ 47
4.3.3 Determination of frequencies of life sign for data taken in corridor with
plywood wall .......................................................................................................... 51
4.3.4 Determination of location for data taken in room with Brick wall .......... 53
4.3.5 Determination of frequencies of life sign for data taken in room with
Brick wall ............................................................................................................... 55
4.4 Conclusion ....................................................................................................... 57
Chapter 5 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems .................................................. 58
5.1 Experimental setup with 10 cm brick wall ...................................................... 58
5.2 Data collection with 10 cm thick brick wall ................................................... 58
5.3 Signal Processing Algorithm ........................................................................... 60
5.4 Result and Discussion ..................................................................................... 66
5.4.1 Absence of target behind brick wall ........................................................ 66
5.4.2 Presence of target behind Brick wall ....................................................... 67
5.4.3 Raw Image (Amplitude versus Slow time variation) ............................... 68
5.4.4 Improvement in the detection using Clutter reduction technique ............ 68
5.4.5 Location Determination using standard deviation ................................... 73
5.4.6 FFT based Results .................................................................................... 74
5.5 CONCLUSION ............................................................................................... 75
Chapter 6 Effect of Thickness of wall on detection of location and breathing
frequency of human being standing behind the brick wall ............................................. 77
6.1 Experimental Setup ......................................................................................... 77
6.2 Data collection................................................................................................. 78
6.2.1 When thickness of brick wall is 0.32 m ................................................... 78
6.2.2 When thickness of brick wall is 0.22 m ................................................... 79
6.3 Signal processing technique ............................................................................ 80
6.4 Result and Discussion ..................................................................................... 82
6.4.1 External calibration .................................................................................. 82
6.4.2 When thickness of brick wall is 0.32 m ................................................... 83
6.4.3 When the thickness of brick wall is 0.22 m ............................................. 95
6.5 Conclusion ..................................................................................................... 106
Chapter 7 Conclusion ............................................................................................... 109
7.1 Summary of contributions ............................................................................. 109
7.2 Below is a list of the major contributions in this research: ............................ 110
7.3 Suggestions for Future Work ......................................................................... 111
References ................................................................................................................113
Appendix A :Authors Contributions ........................................................................... A-1
A.1 Conference Publications ................................................................................A-1
A.2 Journal Contributions .....................................................................................A-1
Appendix B :Photographs ............................................................................................ B-1
List of Figures
Fig. 1.1A SFCW radar waveform ................................................................................. 5
Fig. 1.2 Resolution (a) Range resolution (b) Cross range resolution ......................... 9
Fig. 3.1 Experimental Setup ......................................................................................... 22
Fig. 3.2 Flowchart for Signal processing steps ........................................................... 25
Fig. 3.3 Range profiles ................................................................................................. 29
Fig. 3.4 Raw Image ..................................................................................................... 29
Fig. 3.5 Amplitude variation at target location ......................................................... 30
Fig. 3.6 Frequency spectrum using FFT ..................................................................... 31
Fig. 3.7 Time-frequency plot after HHT ..................................................................... 31
Fig. 4.1 Experimental Setup using Monostatic Radar............................................... 36
Fig. 4.2 UWB Ridge Horn Antenna ............................................................................ 40
Fig. 4.3 Connection with Circulator for calculation of insertion loss ...................... 41
Fig. 4.4 Experimental Setup with plywood wall......................................................... 43
Fig. 4.5 Experimental Setup with brick wall .............................................................. 43
Fig. 4.6 Flowcharts for Signal Processing ................................................................... 45
Fig. 4.7 Metal calibration (a) When Antenna to Metal distance is 0.5m, ................ 47
Fig. 4.8 Distance between Antenna and wall is 0.5m and between wall and target is
1.5m .................................................................................................................. 48
Fig. 4.9 Distance between Antenna and wall is 1m .................................................... 49
Fig. 4.10 Distance between Antenna and wall is 1.5m ............................................... 50
Fig. 4.11 Frequency Spectrum of human being standing behind plywood wall at
different distance ............................................................................................ 52
Fig. 4.12 Distance between antenna and Brick wall =0.5m ....................................... 54
Fig. 4.13 Distance between Brick wall and Human target 0.5m ............................. 54
Fig. 4.14 Frequency Spectrum of human being standing behind plywood wall at
different distance ............................................................................................ 56
Fig. 5.1 Experimental Setup with VNA, two antennas and human subject standing
behind brick wall ............................................................................................ 59
Fig. 5.2 Flow-chart for processing steps ..................................................................... 64
Fig. 5.3 Range profiles for all 1024 traces in absence of target ................................ 69
Fig. 5.4 Range profiles for all 1024 traces in the presence of target ........................ 70
Fig. 5.5 Raw Image ....................................................................................................... 71
Fig. 5.6 Image after clutter reduction......................................................................... 72
Fig. 5.7 Image formed using Target Singular Components ..................................... 73
Fig. 5.8 Frequency Spectrum ...................................................................................... 75
Fig. 6.1 Experimental Setup with two antenna system ............................................. 79
Fig. 6.2 Flow chart of Signal processing technique ................................................... 81
Fig. 6.3 External calibration using Metal sheet ......................................................... 82
Fig. 6.4 Range profile for external calibration .......................................................... 83
Fig. 6.5 For case 1:Distance between wall and human target is 0.5m .................... 84
Fig. 6.6 For case 2:Distance between wall and human target is 0.5m .................... 86
Fig. 6.7 For case 3:Distance between wall and human target is 0.5m .................... 87
Fig. 6.8 Plot of amplitude variation for case 1 data ................................................. 91
Fig. 6.9 Plot of amplitude variation for case 2 data ................................................. 91
Fig. 6.10 Plot of amplitude variation for case 3 data ............................................... 92
Fig. 6.11 Breathing frequency extraction using FFT for case 1 dataset.................. 93
Fig. 6.12 Breathing frequency extraction using FFT for case 2 dataset................. 94
Fig. 6.13 Breathing frequency extraction using FFT for case 3 dataset................. 95
Fig. 6.14 For case 4: Distance between wall and human target is 0.5m ................. 96
Fig. 6.15 For case 5: Distance between wall and human target is 0.5m ................. 97
Fig. 6.16 For case 6: Distance between wall and human target is 0.5m ................ 99
Fig. 6.17 Breathing frequency extraction using FFT for case 4 dataset............... 102
Fig. 6.18 Amplitude extraction using SD for case 5 dataset .................................. 103
Fig. 6.19 Amplitude extraction using SD for case 6 dataset .................................. 103
Fig. 6.20 Breathing frequency extraction using FFT for case 4 dataset............... 104
Fig. 6.21 Breathing frequency extraction using FFT for case 5 dataset............... 105
Fig. 6.22 Breathing frequency extraction using FFT for case 6 dataset............... 106
List of Tables
Table 3.1 – Radar Parameter for Experimentation ................................................... 22
Table 3.2 SD values at Different location .................................................................... 28
Table 4.1 Mono-static Radar Parameters ................................................................... 37
Table 4.2 Data collected with various distance between Antenna and plywood wall
and also variation in distance between plywood wall and target. .............. 42
Table 4.3 Data collected with various distance between antenna and brick wall and
also variation in distance between brick wall and target .......................... 42
Table 4.4 Location determination when observation taken in corridor with
Plywood Wall .................................................................................................. 51
Table 4.5 Frequency determination when observation taken in corridor with
Plywood Wall .................................................................................................. 52
Table 4.6 Location determination when observation taken in room with Brick Wall
........................................................................................................................................ 55
Table 4.7 Respiration Frequency determination when observation taken in room
with brick wall ................................................................................................ 57
Table 5.1 SFCW radar parameters with two antennas ............................................. 60
Table 5.2 Matrix representing organization of data collected .................................. 60
Table 5.3 Location of target obtained using SD ......................................................... 73
Table 6.1 Data Collection details for 0.32 m Brick wall taken with two antenna
system .............................................................................................................. 79
Table 6.2. Data Collection details for 0.22 m Brick wall taken with two antenna
system .............................................................................................................. 80
Table 6.3 Position and amplitude when distance between Antenna and Brick-wall
is 0.5m .............................................................................................................. 84
Table 6.4 When distance between Antenna and Brick-wall is 1m ........................... 86
Table 6.5 When distance between Antenna and Brick-wall is 1.5m ........................ 87
Table 6.6 Location obtained from Experimental data after velocity correction ..... 89
Table 6.7 Breathing frequency values for different datasets ................................... 95
Table 6.8 Position and amplitude when distance between wall and target is 0.5m 96
Table 6.9 Position and amplitude when distance between wall and target is 1m... 98
Table 6.10 Position and amplitude when distance between wall and target is 1.5m
........................................................................................................................................ 99
Table 6.11 Location obtained from Experimental data ......................................... 100
Table 6.12 Breathing frequency values for different datasets ............................... 106
Nomenclature
2D Two dimensional
ACC Adaptive clutter cancellation
AGC Automatic Gain Controller
BW Band width
CFAR Constant false alarm ratio
CPI Coherent processing interval
CR Cross Range
CT Curvelet Transform
CW Continuous wave
EM Electromagnetic
EMD Emperical mode decomposition
FAR False Alarm Rate
FDTD Finite difference time domain
FFT Fast Fourier Transform
FIR Finite impulse response
FM Frequency modulation
FMCW Frequency Modulation Continuous Wave
GPR Ground Penetrating Radar
HPBW Half Power Beam width
HHT Hilbert Huang Transform
HOS Higher order Statistics
ICA Independent Component Analysis
IFFT Inverse Fast Fourier Transform
IMF Intrinsic mode function
MA Moving average
MUSIC Multiple Signal Classification
MSE Mean Square Error
MTI Moving target indicator
PC Principle Component
PDF Probability Density Function
PSD Power Spectral Density
PSNR Peak Signal to Noise Ratio
Radar Radar Ranging and Detection
RCS Radar Cross Section
RF Radio Frequency
RGK Radial Gaussian kernel
RPF Recursive pixel finding
SAR Synthetic Aperture Radar
SFCW Step Frequency Continuous Wave
SNCR Signal to Noise clutter ratio
SNR Signal to Noise Ratio
SP Specificity
STFT Short time Fourier Transform
SVD Singular Value decomposition
TOSM Though open short and Match
TWI Through wall imaging
TWRI Through-the-Wall Radar Imaging
UWB Ultra wide band
VNA Vector Network Analyzer
Chapter 1 Introduction
1.1 Motivation
A human kind was always interested to find out the unknown from the very beginning
of the mankind history. Our eyes help us to investigate our environment by reflection of
light. However, a wavelength of visible light allows a transparent view through only
non opaque materials such as glass and not through opaque materials. On the other
hand, Electromagnetic (EM) waves in microwave frequency range, with the exception
of metal are able to penetrate through almost all types of materials around us such as
plywood, plastic, brick wall, concrete wall, etc.
Through-the-Wall Radar Imaging (TWRI) with EM waves is an emerging technology,
allowing to “seeing” through visually opaque material such as walls. Identification of
living and non-living targets using TWRI has lots of applications in various field of
healthcare, defence, monitoring human activities backside of walls in connection with
law enforcement, surveillance, search-and-rescue mission operations and natural
disasters.
Especially when the person is trapped under rubble due to earth quake or due to
collapsed buildings, remote and contactless detection of human vital life signs via radar
sensing is very useful for search and rescue operations.
The trapped victim under such circumstances cannot make movement and the only way
to detect the presence of life is to detect life sign signals as early as possible to reduce
the loss of life. The life signs which are normally used for detection are respiration rate
and heart rate.
In recent years, the research is mainly directed toward sensors based on microwave
radar system for the detection of life sign [1-12]. Ultrasound, millimeter wave
2 Introduction
radiometry, infrared, and X-rays can be used for through wall imaging but RADAR
sensor is the most suitable due to the following reasons. Ultrasound technique can be
used to detect and find target behind a metallic or non-metallic wall but cannot be used
for imaging due to the high resolution requirement in TWRI. Millimeter wave
radiometer uses energies radiated by bodies of targets for detection but limitation is that
it works only up to very short distances [13]. Infrared can be used to image the target
through wall at very short distance as attenuation through a wall is very high. X-ray
based sensors provide good imaging quality but are limited due to their high cost and
lack of medical safety. Researchers are using either impulse based radar system or
Continuous wave radar system [14]. This work relates to the detection of human being
life signs from behind the wall using step frequency continuous wave (SFCW) based
radar system because of its advantages over impulse radar [15]. The TWI Radar system
is used for scanning of the living objects behind a wall. An electromagnetic wave is
transmitted via antenna system, penetrates through the wall, it is reflected by the
investigated object, penetrates again through the wall, and is received back via receiver
antenna. The reflected signal is received by the radar and corresponding data are
collected. Then data processing is carried out to find out information about the living
targets. These targets are located behind a wall or any other visually opaque medium.
The simplest problem is just to find out the location of targets behind the wall. One may
further classify living targets and others on the basis of micro-Doppler characteristics
(breathing and heartbeat). After that one may further generate a 2D image of the target
which contains information about the target‟s lateral extent and its location.
One of the main problems in detection of life sign is strong reflection of brick wall and
antenna air coupling. These reflections are unwanted and are called as Clutter. Different
clutter reductions techniques are used like background subtraction method, range
gating, moving average reduction technique, notch filtering and singular value
decomposition (SVD) [16-18]. SVD has been used by researchers working in through
wall radar imaging to remove the wall clutter and to detect behind-the wall targets [16].
In [17], SVD is applied on the data collected by taking measurements at different
1.1 Motivation 3
antenna positions, whereas in this work; we have applied SVD on data measured at
same antenna location. Though wall and target reflections reside in many Eigen images,
the significant or dominant Eigen images are obtained from first few Eigen values.
Target signal strength can be improved by removal of significant wall reflection using
most significant singular values. In [9], SVD has been used for life sign extraction of
more than one target but it is used after taking Fast Fourier Transform (FFT) of
correlated signal. In our work we are proposing use of SVD for clutter reduction in
earlier stage i.e., before applying FFT.
Other effects due to presence of wall are refraction; attenuation and change in velocity
of the signal which poses challenges for detecting and localization of life sign of human
being. Researchers have carried out work on detecting and extracting life sign of human
being using different signal processing techniques which is briefly given here.
Various types of signal processing algorithms or techniques are used for life sign
extraction [17]. Change detection algorithm was used for detecting life sign of human
[21]. Life detection algorithm using step frequency continuous wave (SFCW) radar is
developed in [22-23]. In [24], they have proposed the life sign detection algorithm for
SFCW radar in which multi-periods and filters are used to extract the micro-vibrations
parameter of the life targets. Remote detection of human vital sign is investigated using
SFCW based radar system [15]. In this paper various factors such as effect of varying
thickness of the obstacles, human subject postures, status of breathing, position of radar
antenna relative to human subject‟s chest, as well as the length of survey times are
studied. Empirical mode decomposition (EMD)-Hilbert Huang transform (HHT)
method for detection of breathing as well as other motions is used [25-26]. On the basis
of specific literature survey on SFCW based radar; it is observed that no one has
applied EMD- HHT technique for extraction of life sign signal on experimental data.
In a nutshell, considerable amount of attention and research work is required for
dealing with different issues related to life sign detection. This motivated us to develop
signal processing techniques to improve detection, localization and extraction of life
sign of the living targets.
4 Introduction
1.2 Basic and Operating Principle of SFCW Radar
Ferris and Currie [13] had carried out a survey of technologies applied for through the
wall surveillance. The technologies used for through wall imaging are classified as
active or passive imaging. In active imaging, RF, acoustic, optical or X ray energy is
used to estimate the reflectivity distribution of a remote scene and in passive,
millimeter wave imaging radiometer is used which uses energies that are radiated by
the bodies of persons within a building for detection [14].
The Stepped Frequency Continuous Wave (SFCW) radar was not included in the table.
Due to complexity of system and higher component cost, SFCW radars were not
popular earlier. However, the cost of RF technologies has been decreasing
considerably, making it more feasible to use SFCW radars. Now a days, SFCW and
short pulse radar represent two different techniques which are used to generate a wide
band of frequencies for detection of hidden objects under some barrier. SFCW radar is
a frequency domain system whereas pulse radar is time domain System. The SFCW
radar system has several advantages over time domain systems [15].
SFCW is a frequency-domain pulse synthesis method that has been widely applied for
radar systems. The SFCW radar operates from a low frequency limit to a high
frequency limit and the digital radio technology is used to provide this stepping up. The
step frequency waveform can be described as an intra pulse version of the common
linear FM pulse compression waveform. A series of N coherent pulses are transmitted
whose frequencies are monotonically increased from pulse to pulse by a fixed
frequency increment Δf. The frequency of the nth (and is varied from 0 to N-1) pulse
can be written as Eq.(1.1). A stepped frequency continuous wave is shown in Figure
1.1.
fnff on (1.1)
where fo is the starting carrier frequency of system and Δf is the frequency step
size, that is, the change in frequency from pulse to pulse. Each pulse is seconds wide,
and the time interval T between the pulses is adjusted for ambiguous or unambiguous
range. Note that the frequency remains constant within each pulse. Groups of N pulses
1.2 Basic and Operating Principle of SFCW Radar 5
(burst) are transmitted and received before any processing is initiated to realize the
high-resolution potential of this waveform.
Fig. 1.1A SFCW radar waveform
The burst time is defined as: “The time corresponding to transmission of N pulses will
be called the coherent processing interval (CPI)” [19].
The instantaneous bandwidth of this waveform is nearly equal to the inverse of the
pulse width and it is much less than the effective bandwidth. Pulses of typical time
duration have narrow bandwidths, thus making the instantaneous bandwidth of the
radar narrow. However, effective large bandwidth can be realized by appropriately
processing the N pulses in a CPI. The waveform's effective bandwidth, denoted as in
contrast, to the instantaneous bandwidth is determined by the product of the number of
pulses N and frequency step size Δf as in Eq. (1.2),
fNBeffec (1.2)
The range resolution for any waveform depends on this effective bandwidth of the
waveform. The range resolution of the SFCW, in meter is given as;
effecB
CR
2 (1.3)
The range resolution can be made finer by either increasing the number of pulses and/or
increasing the frequency step size. Thus, two objects separated in the down range by a
distance greater than can be detected as two distinct objects.
One of the facts about step-frequency radar is that its resolution does not depend on the
instantaneous bandwidth, and that resolution can be increased arbitrarily by increasing
NΔf. There is a constraint on selection of Δf (i.e. 1f ); however, N can be
increased to achieved the very high range resolution. It should be noted that, fine range
6 Introduction
resolution is achieved by large bandwidth irrespective of the waveform and the
compression method used. So large bandwidth is achieved by sequentially over many
pulses, by inter pulse frequency modulation whereas, for conventional radars, it is
achieved in a single pulse by intrapulse phase or frequency modulation.
A step-frequency waveform achieves wide bandwidth (NΔf) sequentially (over a burst
of many pulses) but has a narrow instantaneous bandwidth of 1/ . It provides the high
range resolution of wideband radar systems with some of the advantages over
narrowband radar systems. Step-frequency radar achieves range resolution of c/2NΔf
(equivalent to a bandwidth of NΔf) as compared with range resolution of c/2 for
constant-frequency waveforms.
The SFCW signal is transmitted through an antenna and scattered signals from the wall
and the target can be collected using antenna or antenna array. This raw frequency data
is then sent through inverse fast Fourier transform (IFFT) to yield the scattering from
individual targets. Using electromagnetic theory and signal processing these signals can
be analyzed and an image of the scene behind the wall can be produced.
The main advantage of the stepped frequency technique is the need of low speed analog
to digital converters making it relatively easy with current technologies to efficiently
sample ultra wideband signals. Measuring an object reflection in the frequency domain
and applying an inversion technique to obtain the time information of physical space
removes the requirements for wide instantaneous bandwidth and high sampling rate,
leading to reductions in physical size, weight, complexity and cost of the radar
hardware [20]. The other advantages are high dynamic range, high signal to clutter ratio
and low power consumption.
1.3 SFCW Radar Parameters
The SFCW radar parameters for the life sign detections through barrier should be
chosen carefully which are described below.
Number of frequency points
1.3 SFCW Radar Parameters 7
SFCW radar illuminates target with consecutive train of number of frequencies and
process it coherently after receiving them. Thus the process gain will be high, if number
of frequency points is high. The choice of high frequency points result in small
frequency step size for better resolution. If the number of points is chosen smaller then
data acquisition time is reduced.
Unambiguous range
The maximum range for receiving transmitted radar signal after reflection prior to next
transmittion of pulse is,
i.e. the unambiguous range is given by
fu
cR
2 (1.4)
From equation (1.4) if the frequency step is narrow then ambiguous range will be
greater. Frequency step size ∆f is also calculated as
)1(
N
BWf (1.5)
Beam width of antenna
In monostatic radar system, with synthesize aperture techniques; the beam width of
single antenna should be narrow. If the antenna beam is narrow it is easy to pick line of
sight target signal.
Wall parameters
The wall through which the signal is penetrating plays an important role in detection.
To ensure signal penetration through wall it is desirable to have minimum attenuation at
the working frequencies. The other parameters of wall which are important to combat
the effect of wall such as shift in target position and blurriness in image are thickness of
wall and dielectric constant. These parameters should be known before hand for
processing.
Target parameters
8 Introduction
Behind the wall there are two possibilities of the targets i.e., moving or stationary
human being. Microwave signal both penetrate and get reflected off from target
material. The composition and thickness of the targets are prime factors for receiving
the reflected signal. If the dielectric constant of target is high then reflection will be
high whereas for low dielectric, reflections seem to be very poor. In this report, the
stationary human being is considered since project is about detecting trapped human
being who cannot make movement and is live. The life signs are gauge of survival of
human being. The vital signs which are mainly used are respiration rate and heart rate.
The sensor system should aim at detecting and extracting the heartbeat and the
respiration rate of a human subject. Average human being breathes about 15 to 20 times
per minute and has 60 to 72 heartbeats per minute. This corresponds to the chest-wall
motion with a frequency of 0.25 to 0.33 Hz for respiration and frequency due to beating
of the heart will be 1 to 1.2 Hz.
Down Range Resolution
Down range resolution is the capacity of the radar to discriminate individual elements
that are close to each other in down range as shown in Figure 1.2 (a). High down range
resolution is obtained by using wide bandwidth and is given as equation (1.3) [19].
The effective bandwidth is determined by the total frequency excursion, i.e., N*∆f. The
down range resolution of step frequency radar is given by equation (1.6)
fN
cR
2 (1.6)
where N is number of frequency points and ∆f is step size.
The required bandwidth must be greater than 1 GHz to obtain range resolution in order
to detect object size of few centimeters. The actual value is taken more than theoretical
value.
Cross Range Resolution
Cross range resolution is the capacity of the radar to discriminate individual elements
that are close to each other in cross range as shown in Figure 1.2(b).
1.3 SFCW Radar Parameters 9
R
∆R
Antenna
R
Antenna
ΔCR
(a) (b)
Fig. 1.2 Resolution (a) Range resolution (b) Cross range resolution
Resolution in cross range is a function of wavelength at the lowest operating frequency,
the length of physical antenna aperture and distance to target. Cross range resolution is
defined as
D
RCR
(1.7)
where is wavelength, R is distance to target in far-field from antenna and D is
physical aperture of antenna. For a real antenna, cross range resolution degrades with
increasing target distance. To achieve high cross range resolution, narrow beam width
is required for which the antenna aperture should be quite large which is physically
unmanageable. Another approach is to introduce the concept of a fixed array or
synthetic array. The idea of synthetic array is that a physical antenna moves to each
point. Processing the data allows us to synthesize an effective aperture many times the
size of a real aperture. Thus, the distance travelled during data observation determines
aperture size, limited by time required to scanning. If fixed array is used scanning time
is reduced with increase in complexity of processing signals.
High frequency range is chosen at which the narrow beam width of antenna is achieved.
Thus high cross range resolution requirement leads to the selection of higher frequency.
But at high frequencies, penetration through the wall is low. Thus there are inherent
tradeoffs between resolution and penetration. Better resolution and penetration are the
10 Introduction
major challenges being faced in detection of life sign through wall. Various types of
wall materials are used in different parts of the world like wood, asbestos, brick,
concrete and so on. The characterization of common types of wall is described by [27].
The walls made of wood are approximately transparent to radar frequencies. Thus
frequencies above 10 GHz can be used for imaging. On the other hand in brick wall
attenuation is more. In brick wall, one way attenuation is reported as 5 dB/cm at 5 GHz
and in concrete it is 10 dB/cm at 3 GHz [14].
Selection of parameter for assembling radar system
Selection of frequency range up to approximately 3 GHz can be used as attenuation is
within acceptable range. On other hand bandwidth is chosen so as to resolve the targets
in down range in tenth of centimetres. So the selected frequency range of 1 GHz to 3
GHz with bandwidth of 2 GHz, number of frequency points as 201, gives range
resolution of 7.5 cm in air according to equation (1.6). The maximum distance to target
is calculated using radar range equation (1.8)
22
4max2
3)4(
a
win
G
RLSNRBTekPt (1.8)
where,
Pt=minimum required transmitted power to fulfil the detection criteria,
K=Boltzmanns constant,
Te =Equivalent noise temperature referred to the receiver input,
B=Transmitted bandwidth,
SNRin=Signal to noise ratio at the input of the receiver,
L2w=Two way attenuation due to propagation through the wall,
Rmax=Range of target to be detected ,
Ga=transmitter receiver antenna gain,
Wavelength,
Radar cross section of the target to be detected,
1.3 SFCW Radar Parameters 11
With the help of above equation (1.8), keeping the transmitted power constant, range is
calculated for different types of barriers having variable values of attenuation. While
calculating range, the following values of various parameters are taken.
Pt= 10dB, Ga = 9.9db , kTe=200dB, 2 -18.4dBsm for 2.5 GHz center frequency,
=1sq.m, the two way losses due to wall are taken to be, 15dB, 18dB, 20dB and 23dB
respectively for wood wall, brick, stone and concrete wall. The range is obtained as
2.93m, 2.89m, 2.88m, and 2.84m respectively. Thus as attenuation due to wall
increases, range reduces.
Organization of report is as follows. Brief Review of Literature is given in chapter 2.
Chapter 3 describes about experimental work in which S11 (reflection coefficient) is
measured using single antenna to detect and extract breathing frequency of human
being hidden behind plywood wall. In Chapter 4, instead of S11, S21 is measured using
single antenna with the help of circulator to detect and extract breathing frequency of
human being hidden behind plywood and brick wall. Chapter 5 describes about work in
which S21 is measured using two antennas i.e., transmitting and receiving to detect and
extract breathing frequency of human. The effect of thickness of brick wall on detection
of life sign is carried out in chapter 6. Finally concluding remarks are given in Chapter
7.
Chapter 2 Literature Survey
2.1 Introduction
Non contact Detection and monitoring of Human life sign is cardinal issue in many
applications such as remote non contact health monitoring, homeland and military
security, localization and rescue of living survivor buried under rubbles and debris of
building in post earthquake disaster scenario, localization or detection of invisible
human being hiding behind the wall etc. With the significance of this topic there has
been growing interest of researchers in this field in recent years which has resulted in
large improvement in the technology used.
2.2 Review of work related to type of Radar used
Radar signals reflected from the living target bears the information about their
vital sign. In this technology living human body micro motion because of heartbeat and
respiration results in modulation of incident radar signals which are demodulated to
extract vital sign. These vital sign detection can be achieved by using continuous wave
(CW) [15],[28]–[32] and ultra wide band (UWB) [4][5][6][28],[31]–[49] techniques.
Frequency domain signals techniques such as CW radar [28],[31],[32] and frequency
modulated continuous wave (FMCW) radar [29],[30] and time domain signal
techniques like UWB pulse radar [4],[5],[31],[33],[34],[37],[38],[42],[43],[45]–
[46],[48] and UWB noise radar[35],[44] and step frequency continuous wave (SFCW)
radar [6],[40],[41],[47] have been used in recent times by different research groups.
CW radar has reported detection life sign but could not locate it [34],[48] though it has
been demonstrated by using FMCW radar system to locate the target using background
subtraction method [15] with resolution of lower value [38] since it cannot easily
separate multipath signals or strong reflections from static barrier such as wall.
14 Literature Survey
Compared to this, pulsed radar can detect and locate multiple vital signs [40] and have
better resolution because of large bandwidth under low SNR [4],[31],[33],[42],[43].
Impulse radar has fast data acquisition though dynamic range is not good as SFCW
radar moreover later has good resolution of range and power [37],[40].These key
advantages and features associated with SFCW radar led the researchers
[6],[40],[41],[49] to utilize SFCW radar for life sign detection behind wall.
2.3 Review of work related to clutter reduction
While detecting and locating vital signs such as breathing or respiration and
heart beat, signals reflected from the target might be completely overridden by noise
and clutter and thus necessitates processing of this signal to improve signal to clutter
ratio and SNR. With typical breathing frequency of 0.1 to 1 Hz and heart beat
frequency of 1 to 3 Hz Band pass filtering suitable to pass 0.1 Hz to 3 Hz and to
eliminate DC components present in signal because most of the clutter power is
concentrated about the zero frequency band i.e. DC component removal of these low-
frequency noise by using Band pass filtering have been proposed [6],[31],[32],[40],
[42],[45],[49].Reflected signals from the target from behind the wall are highly
contaminated with signals reflected from the wall and produce stationary clutter .To
reduce the effect of this stationary clutter and improve signal to clutter ratio background
subtraction method has been illustrated [30], [44].In this scans of received signal
without target behind barrier are subtracted from scan with target behind the barrier to
mitigate the effect of barrier whereas it is impossible in practical situation to get data
without target condition. Further Spatial filters such as Moving Average (MA) filter
background subtraction which notches out the zero spatial frequency component has
been effectively used for removal of clutter signal from wall [46], [48]. Meanwhile the
problem of ripples in MA has not been discussed. Clutter suppression has been
achieved by simple method of subtracting each of the range profile from previous to
eliminate components that have not changed [28].A simple low pass FIR filter called
Moving Average Filter has been proposed [42],[43]which is used to smooth the data by
taking an average of samples of input data to produce single output thus eliminating
2.4 Review of work related to Signal Processing 15
noise. Most of the clutter is associated with zero frequency or DC term and its multiple
integers of pulse repetition frequency thus suppression clutter by offering deep stop
band to these frequencies have been reported by using MTI filtering [6],[41],[49]where
mean of all range profile is subtracted from current one to get moving part. Instability
of time base caused by imperfections in the triggering unit of an impulse radar causes
amplitude instability of impulses and this gradual change in amplitude with time is
linear trend which are actually unwanted amplitude variations as they are not from the
human target and should be removed to improve performance. Linear Trend
Subtraction LTS method have been demonstrated to suppress this pulse amplitude
variation in [4],[42],[43],[45] . However all the signal processing techniques above are
suitable for static clutter suppression and cannot be used for non static clutter
cancellation.
Improvement in signal to clutter ratio have been reported by using an Eigen
structure technique SVD (singular value decomposition) which decompose the received
radar signal into subspaces of clutter and target [4],[39].But in SVD only fundamental
harmonics of signal are considered overlooking signals multiple harmonic components.
Curvelet transform (CT) which is a multi-scale analysis algorithm suitable for 2D data
and which has good analyzing capability of line and curve edges has been used for
clutter and source receiver direct coupling wave noise suppression[4]. A linear phase
filter with constant phase delay has been applied to separate out noise components
[45].In [46] adaptive clutter cancellation (ACC) algorithm has been proposed to remove
respiration like clutter.
2.4 Review of work related to Signal Processing
To detect the breath or respiration frequency and heart beat frequency and to
locate the same under severe clutter contamination after pre-processing of received
echo signal from the target laying behind the barrier researchers have proposed many
signal processing algorithms.
16 Literature Survey
FFT gives data about human micro and macro movement‟s frequency and amplitude. In
micro motion lies vital sign information. Extraction of vital sign has been demonstrated
with FFT algorithm [4],[6],[28],[29],[31],[36],[37],[39]–[41],[43],[49]. In [40]
respiration frequency has been detected for different positions of human target behind
wall of 5 cm to 20 cm thickness but there is further scope in developing a method for
faster data collection and improvement in signal processing efficiency. Ellipse-cross
localization has been illustrated to locate human target [49].FFT used in
[28],[29],[40],[49] doesn‟t give time dependent information for non-stationary signal
also gives distorted peaks in frequency domain. Thus it is not suitable to analyze a
narrow frequency band of a non stationary signal.
In [41] it has been demonstrated to detect life sign behind the corner of wall
considering diffraction of signals at corner with SFCW radar of 10 GHz center
frequency and STFT signal processing which maps a signal into a function of time and
frequency .Breathing signal has been detected with STFT [31],[39],[48].However the
results are from ideal conditions and multipath data has not been addressed which can
be possibly used for positioning by combining multiple detections of a target,
corresponding to different propagation paths[31],[39]. In [39]experiments has been
performed on Gypsum wall, Brick wall, wooden door, concrete wall with UWB pulse
radar with center frequency of 4.3 GHz. Heart beat frequency and breathing frequency
has been detected in all cases except concrete wall and gypsum when SVD and STFT
was used but monostatic mode limits the use of spatial filtering to reduce clutter also
STFT has got fixed resolution.
S-transform is the signal processing technique whose performance is dynamic in the
sense that it doesn‟t have fixed resolution as was reported with STFT and thus [36]
have described life sign detection and location using ST. In [36] life sign extraction and
location of human target has been achieved by 2-D FFT method to analyze the
frequency of the cross range data and ST with an experiment in which a brick of wall
thickness 0.2 m and 1 to 4 GHz frequency UWB radar but better performance could
have been achieved with better accuracy if noise signal have been avoided.
2.4 Review of work related to Signal Processing 17
FFT and HHT has been utilized over the data collected from physical experiment
performed in laboratory with 20 cm concrete cinderblock wall and 1GHz signal source
and by the FDTD simulation data [37].With use of HHT non linear and non stationary
signals have been decomposed to extract respiration and heart beat signal considering
the condition of two human targets with normal breathing, holding the breath and
repeatedly speaking the word “one, two, three”. Though respiration frequency has been
detected it hardly detects heart beat frequency. It has also reported that the subject
under test should be steady while taking readings or its motion frequency will override
vital sign frequency [37].Use of HHT has also been proposed in[5],[35], [44].In [5]an
experiment using pulsed radar with 1GHz frequency and cinder block wall about 1.5 m
was used and results have been validated with FDTD data.
With same experimental values author of [36] have demonstrated the extraction and
location of breathing and heart beat signal in complex condition as of earthquake rubble
in [47] with FFT and Hilbert-Huang transform also with FFT and RGK methods(radial
Gaussian kernel) but here is scope of improving resolution i.e. better detection.
Nonlinear and non-stationary data analysis has been achieved using HHT [37] to detect
respiration signal and results are validated with FDTD simulation data but it has failed
to detect heart beat signal.
The HHT is based on the principle of empirical mode decomposition (EMD) which
decomposes signal into a collection of intrinsic mode functions (IMF) and the Hilbert
Transform which gives energy of each instantaneous frequency. At the low-frequency
region in HHT method the EMD will generate undesirable IMFs that may cause
misinterpretation to the result. Also the first obtained IMF may cover very wide
frequency range and prevents it from achieving mono component property and the
EMD operation cannot separate signals that contain low-energy components.
Authors of [50] have described a method based on MUSIC algorithm applied to echo
data collected from CW radar with 10 GHz centre frequency to detect breathing signal.
MUSIC detects frequencies in a signal by performing Eigen decomposition on the
covariance matrix of a data vector of M samples obtained from the samples of the
received signal. This has been further modified by authors in [32] where SNR is
18 Literature Survey
improved compared to previous method utilizing smoothing procedures referred to as
temporal and sample de-correlation considering no perfect periodicity of received
signal. Moreover, this method requires less number of computations. Compared to the
old method significant improvement in the value of Dynamic range and noise rejection
has been reported in [32]. This algorithm detects only main harmonic components of
spectral noise since it can add or delete close harmonics placed in not pre known
position. Thus this can be used to detect particular harmonic component with presumed
frequency. However, this method fails to work in case of coloured noise to determine
the number of sinusoids considering colored noise as additional sinusoids. Higher-order
statistics have been applied [33] to this problem as they show robustness to coloured
Gaussian noise.
Gaussian noise and its harmonics have been suppressed by taking FFT of FOC to detect
and locate breathing frequency behind 0.28m wall [6].Method of detecting and locating
vital sign using multiple higher order cumulant has been presented in [33].It has
produced results with high value of SNR and also suppressed higher harmonics to
automatically detect vital sign. Further in [33] clutter with same frequency as
respiration and heartbeat were found. The second-order measures[32], [50] work fine if
the signal has a Gaussian (Normal) probability density function, but many real-life echo
signals are non-Gaussian and this Second-order measures (such as the power spectrum
and autocorrelation functions) contain no phase information. Complete characterization
of Gaussian signal can be achieved by its mean and its variance. Higher order statistics
(HOS) of Gaussian signals are either zero or contain redundant information and many
of echo signals have non-zero HOS and many noises are Gaussian thus in principle the
HOS are less affected by Gaussian background noise than the 2nd order measures like
MUSIC.
A method based on K-mean clustering algorithm and constant false alarm ratio to detect
and locate human vital sign in very low SNCR condition has been proposed [4].
However in an experiment to detect two human subjects this method failed to detect
farther subject also this algorithm works on condition that number of subjects are
known and CFAR window in fast time cannot detect life sign near start and stop point.
2.4 Review of work related to Signal Processing 19
These issues have been resolved in [43].Here Automatic gain control (AGC) method
which suppresses signals with strong power and enhances signals with low power and
Recursive Pixels Finding (RPF) algorithm has been suggested which calculates number
of life signs and also removes clutter signals with same frequency and energy as that of
respiration frequency further there is scope to suppress all type of clutter in complex
real environment. In these works however authors have focused only on fundamental
harmonics of respiratory signals rather than its rest of harmonics with which better
detection could have been achieved.
20 Literature Survey
3.1 Introduction 21
Chapter 3 Extraction of breathing frequency of
human being hidden behind the wall using
different signal processing techniques
3.1 Introduction
The objective of this chapter is to extract life sign signal frequency using FFT and HHT
signal processing algorithm from the data which is collected experimentally using
SFCW radar system.
Organization of chapter is as follows. Section 3.2 describes the methodology in which
experimental setup and radar system parameters used for data collection is included. It
also describes the theory in signal processing algorithms i.e., FFT and HHT methods
which are used for extraction of life sign. Results obtained through Experiments are
presented in Section 3.3. Section 3.4 gives final concluding remarks.
3.2 Methodology
The methodology adopted in this chapter is to collect the data using SFCW radar first
for the person behind the opaque material. After data collection, analysis using signal
processing techniques i.e., FFT and HHT for detecting life sign is carried out. For data
collection, experimental setup is explained in section 3.2.1 and signal processing
techniques are described in section 3.2.2
22 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques
3.2.1 Experimental setup
To extract life sign of human subject hiding behind plywood wall an experimental setup
is developed which is shown in Fig.3.1. SFCW based radar system in a mono-static
mode was used with the help of Vector Network Analyzer (VNA) and single antenna as
shown in Fig.3.1. Calibration of VNA is done by standard one port calibration process
i.e., Open Short Matched before reflection parameter S11 is measured in frequency
range from 1 GHz to 3 GHz. The total numbers of scans or observations that were
carried out are 1024. Data is collected by using VBA Macro program and is transferred
to PC for further processing using MATLAB software.
Fig. 3.1 Experimental Setup
Table 3.1 – Radar Parameter for Experimentation
The obstacle is plywood wall of 12 mm thickness. Distance between antenna and wall
is maintained at 46 cm while distance between wall and human being is fixed at 58 cm.
Sr. No. Radar Parameter and specifications
Parameter Specification
1 Operating frequency range 1 GHz to 3 GHz
2 Radiated Power 0 dBm
3 Number of Frequency points 201
4 Number of traces 1024
5 Horn Antenna Gain 20 dB
6 Antenna Beamwidth 49.68(H Plane),E Plane)
3.2 Methodology 23
The total distance from antenna to target is 105.2 cm. The other radar parameters set for
experimentation are shown in Table 3.1.
3.2.2 Signal Processing
After data collection, processing steps given in the flow chart as shown in Fig. 3.2 are
applied to extract breathing frequency representing life sign. Micro-vibration activities
are observed in every scanning data which can be used to differentiate them from the
static targets. The biologic and static object have much difference in the same range, so
we can extract the vital information and remove the static objects. As to human, the
heartbeat frequency is around 1-2 Hz and the respiration/breathing frequency 0.2-0.5
Hz. The static object's frequency is around 0 Hz. Thus, if we find the frequency in 0.2
to 2 Hz, we can detect/extract the vital information.
Step 1. Read trace data in frequency domain
Data collected by placing an antenna just above the chest of human being is considered.
SFCW radar received the data in frequency domain, and stored in matrix form of
dimension 2011024. To read the first trace, column 1 of data matrix is pick up and so
on for all 1024 traces.
Step 2. Frequency domain to Time domain
. The first trace data is converted into time domain by using Inverse Fast Fourier
Transform (IFFT) [22]. The signal due to single trace after IFFT is given by;
)2exp()()(1
tfjfSts k
K
kk
(3.1)
where t varies from 0 to (K-1)/BW with step interval of 1/BW, BW is bandwidth of the
system , K is maximum number of frequency points and S(fk) is the received reflected
signal in frequency domain at kth
frequency.
Step 3. Time domain to spatial domain
24 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques
The time domain signal is converted into spatial domain. The location of human being
is determined by constructing a range profile in the spatial domain. Range profile is one
dimensional information and given by expression as;
max
1
0)/2(2exp()()( zzczfjfSzs k
K
kk
(3.2)
where c is velocity of light and z is down range distance which is given as
2)( tc
z
(3.3)
Maximum distance calculated using equation (3.4) gives the values as
BWKc
z2
)1(max
(3.4)
But for analysis, maximum distance is taken as per the room dimension i.e., up to 5 m.
Step 4. Stacking all traces
A single range profile will give information about presence of target only. It does not
indicate amplitude variations due to breathing and respiration. For this radar must
illuminate more number of traces. To see the amplitude variations all the range profiles
are stacked one over other. Thus the steps from 1 to 3 are repeated to collect
information from all 1024 traces.
3.2 Methodology 25
Step 1.Read Trace data in Frequency Domain
Step 2. Convert Frequency Domain Data into Time Domain
Step 3.Convert Time Domain Data into Spatial Domain
Step 4.Stack all 1024 traces to Form data matrix
Step 5. Determination of location based on SD
Repeat for all 1024 Trace Data
Step 6. Find Frequency using (a) FFT or (b) HHT
Fig. 3.2 Flowchart for Signal processing steps
Step 5. Determination of location based on standard deviation (SD)
The purpose of processing should be to help the radar operator to clearly understand
whether life sign of a person hiding behind wall is present or absent. The amplitude
value in all the range profiles at different distances should be observed. If there is no
amplitude variation then target is absent.
26 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques
To verify that if amplitude variation is present then target is present, standard deviation
(SD) is calculated at all the distances over all the range profiles using equation (3.5).
1
0
)(1 N
iin x
NSD (3.5)
where n is distances which varies up to 5 m with step size of 0.075 m and N is total
number of traces.
The value of SD at human being location will be highest compared to static objects.
Step 6. Find Frequency using (a) FFT (b)HHT
(a)Fast Fourier Transform Method
The steps below shows the algorithm based on Fast Fourier transform (FFT) method for
extraction of life sign from signal after clutter reduction technique is applied.
Step i: Search for peaks in range profile of each trace and then observe the peak
variations in all the traces.
Step ii: Note the peak location at which we get variations i.e where SD is highest.
Step iii: Extract the signal amplitude from all traces for the peak location obtained in
step ii.
Step iv: Convert the extracted signal in step (iii) (amplitude versus number of traces)
into frequency domain by applying FFT.
In frequency spectrum if we get presence of frequencies in range of 0.2 to 2 Hz, we can
say that a human being life sign is extracted. At the same time, the location of human
being is also obtained.
(b) HHT Method
3.3 Result 27
The HHT is a nonlinear and non-stationary signal analysis technique based on the
combination of the two processes i.e., Empirical Mode Decomposition (EMD) and
Hilbert spectral analysis (HSA) [23]-[25].
The EMD algorithm can be understood from the following steps:
Determine the local extrema (maxima, minima) of the signal and connect the maxima
and minima with an interpolation function to create an upper and lower envelope
respectively.
Calculate the local mean as half the difference between the upper and lower envelopes.
Subtract the local mean from the signal to form the residue.
Iterate step (a) to (c) on the residual until the signal becomes Intrinsic Mode Functions
(IMF). This is repeated until the final residue is a monotonic function.
The signal of interest from the human target can be clearly displayed in the time–
frequency domain. The spectrum is obtained by taking HHT to every IMF, which is
known as Hilbert Spectrum Analysis. Most of the energy is confined in IMF which is
having highest peak power.
3.3 Result
3.3.1 Processing for presence of target behind wall
Data is process according to flowchart described in Section 3.2. Figure 3.3 show range
profiles for all the 1024 traces obtained. The figure is plotted between distances versus
magnitude. From the figure it is observed that first peak is due to weak isolation
between transmitting and receiving antenna, second peak is due to wall reflection and
third peak is due to reflection from human being. The encircled shaded portion
represents presence of target. Since the distance between antenna and target is 1.05 m,
fluctuation in reflections is observed at 1.05 m marked by data tip as shown in Fig. 3.3.
We can observe from Fig. 3.3 that the amplitude of the clutter (i.e., reflection due to
28 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques
antenna air interface, wall reflections, and multiple reflections is higher than reflection
due to human being
3.3.2 Stacking all 1024 traces
All the range profiles acquired are stacked one after another so that two dimensional
images is obtained in which on x-axis, number of traces and on y-axis, down range
distance is plotted. This set of traces is assembled together in a two dimensional data
matrix and visualized as a raw image as shown in Fig. 3.4.
3.3.3 Location determination using standard deviation
To obtain the location of human being, SD is calculated at all locations using equation
(3.5). It is observed from Table 3.2, that at human being location, SD value is high
compared to static object locations like wall due to amplitude variations. Amplitude
variations occur due to respiration and heart beats. The location at which maximum
value of SD is obtained is verified and same as with the location of human being set
during experimental setup.
Table 3.2 SD values at Different location
SD value at Wall location SD value at human being Location
0.0001 0.0055
3.3.4 FFT based results
For better understanding, the amplitude values for detected peaks i.e. peak due to
antenna-air interface, due to wall surface and due to human target are observed from all
1024 traces. It is clearly observed that for the first two peaks i.e. peak due to antenna-
air interface and peak due to wall surface, there is no variation in amplitude for all 1024
traces, while, there is significant amplitude variation at human target location. So in the
next step, only amplitude variation portion is extracted with the help of SD value which
is shown in Fig. 3.5.
3.3 Result 29
Fig. 3.3 Range profiles
Fig. 3.4 Raw Image
30 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques
Fig. 3.5 Amplitude variation at target location
After applying the FFT to the amplitude variations, the result is obtained and is shown
in Fig.3.6. In frequency spectrum, peak is detected at 0.3125 Hz which is nothing but
the breathing frequency (0.2-0.5 Hz) for human. In this heartbeat frequency (1.2-1.7
Hz) was not detected because of small amplitude variation. The FFT result also
includes other clutter frequencies present in signal.
3.3.5 HHT based results
The extracted amplitude variation from peak locations for all the traces is obtained in
the same way as explain in above. The amplitude variation at target location as shown
in Fig. 3.5 is taken as input for EMD. Now, on this result, we had applied the Empirical
Mode Decomposition (EMD) algorithm to decompose it into a finite set of IMF, until
the residue become monotonic as described in algorithm above.
The signal is decomposed into five IMF components. The total energy is decomposed
into different component. Here stoppage criteria used for EMD is squared difference
(SD) [24]. The threshold value 𝜀 is set to 0.3 (typical value). If the squared difference
(SD) is smaller than threshold, the shifting process will be stopped. Hilbert transfer of
IMF gives us instantaneous frequency contained in IMF component. The time-
3.4 Conclusion 31
frequency plot obtained after applying Hilbert transform on IMF 1 is shown in Fig. 3.7.
In time-frequency plot, we got frequency nearly at 0.3998 Hz which is slightly different
from result obtained using FFT method. It is also observed from result of HHT, that all
the energy is concentrated around 0.3998 Hz which is not the case in FFT method.
Hence compared to result obtained using FFT, the result obtained using HHT shows
significant improvement in extraction of breathing frequency.
Fig. 3.6 Frequency spectrum using FFT
Fig. 3.7 Time-frequency plot after HHT
32 Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques
3.4 Conclusion
This chapter demonstrates the extraction of human breathing using two different signal
processing techniques i.e., FFT and HHT. The chapter also described about how the
location of human being is estimated using SD. The experimental results show that FFT
and HHT methods have successfully extracted the breathing frequency of human i.e.,
0.3125 Hz and 0.3998 Hz respectively. Compared to FFT, HHT produce less harmonic
distortions. We have successfully extracted breathing frequency, but heartbeat signal is
not detected. Advanced signal processing technique can be developed to improve the
performance of extraction of heartbeat signal.
4.1 Introduction 33
Chapter 4 Detection of location and breathing
signal of human standing behind brick wall using
monostatic radar system
4.1 Introduction
Quick detection of trapped victims buried under rubble and their subsequent rescue are
big challenges to scientists and technologists. The current study in life sign detection
focuses on i) radar hardware design and ii) developing the SFCW radar signal
processing technique for human vital sign detections in a number of scenarios that
might be pertinent to efficiencies and reliability in earthquake disaster victims search
and rescues. Ultra-wideband (UWB) radar plays an important role in search and rescue
at disaster relief sites. Various types of UWB radar or continuous wave radar are used
for this purpose but SFCW radar technique offers substantial benefits over other radar
systems.
General types of radars systems can be characterized by the relative locations of
the transmitter and the receiver. All radars can be classified as monostatic, bi-static, or
multistatic. A monostatic radar is one in which the transmitter is collocated with the
receiver. In a technical sense, to be truly mono static, the radar must transmit and
receive with the same antenna to maintain the same aperture position and phase center
of the antenna pattern.
Bi-static radar is one in which the transmitter and the receiver are separated by
some distance. In practice, almost all ground penetrating radars are technically bi-static
34 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
since the transmit and receiving antennas are usually separated to provide some
isolation due to the power levels involved. These radars are generally regarded as being
monostatic or pseduo-monostatic since the separation of transmitter and receiver are
small fraction of the range to target. Because the separation of the transmitter and
receiver is small, the angle subtended between the transmitter to target and the receiver
to target is close to zero, a straight, single-shot path.
The objective of this chapter is to use monostatic radar system for the life sign detection
of human being hidden behind wall. The monostatic radar system uses single antenna
for transmission and reception. In this chapter the focus is on measuring transmission
coefficient using VNA in monostatic mode with the help of circulator.
In Section 4.2, description about the methodology adopted is given which consist of
development of experimental setup, data collection and signal processing. The
experimental setup describes how circulator is used in the setup. Data collections
details like number of observations, types of wall used etc are given. The signal
processing algorithm is also described. Section 4.3, describes the results and discussion
obtained on experimental data. Finally Section 4.4 gives conclusions.
4.2 Methodology
The methodology adopted to implement the use of monostatic radar using circulator is
given in steps below.
Step 1 Development of Experimental Setup
Step 2 Data Collection
Step 3 Signal Processing
The detail explanations of each step are given below
4.2 Methodology 35
4.2.1 Development of Experimental Setup
SFCW based radar system in a monostatic mode was assembled with the help of Vector
Network Analyzer (VNA), circulator and antenna as shown in Figure 4.1. In this radar
system, single horn antenna is used for transmission and reception purpose. VNA is
used in frequency range of 2 GHz to 4 GHz with 10 dBm output power. Frequency
range is decided based on the availability of circulator in the same range. The number
of frequency points set are 201 and number of traces that are collected are 1024. Data is
collected by using software VEE pro controlled by PC and VBA macro program and
transferred to PC for further processing. MATLAB software is used for processing.
After calibrating VNA by standard two port calibration process i.e., Through Open
Short Matched (TOSM), the scattering parameters S21 was measured in frequency
domain for all the observations.
The SFCW radar system parameters are given in Table 4.1. The connection
between VNA, circulator and antenna is described here.
Port 1 of VNA is connected to port 1 of circulator. Port 2 of circulator is
connected to Antenna and port 3 of circulator is connected to port 2 of VNA. The
antenna is oriented in vertical polarization for data collection. The antenna was
mounted on tripod. Height of tripod on which antenna is kept is same as chest height of
human being who is standing behind the wall as a target. Types of walls that are used
in the experiments are 2 mm thick plywood and brick wall of thickness 10 cm.
In performing the measurements, single antenna which is used to transmit and receive
is kept at distance away from wall and aligned in line with target for maximum signal
reception. During the experiments, the antenna is placed at different distances away
from wall. The details of main components of the experimental setup are given below.
4.2.1.1 Vector Network Analyzer
In this section, a general background is discussed and the theory of operation of Vector
Network Analyzers (VNA) is explored. In general, network analyzer is a device that is
used to characterize the response of a circuit specifically a linear network to signals of
36 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
various frequencies. A network analyzer can be a scalar type which measures only the
magnitude response of a circuit to stimuli of various frequencies. In practice, scalar
network analyzers are rarely used in modern test and measurement. VNA measure both
the magnitude and phase of the response of a network to stimuli. The measurements are
made of both the reflection of the signal looking into a port and the transmission of
signal through that port. A VNA can be of any arbitrary number of ports. Common
types of VNA are a two-port and four-port. The transmit/receive can be thought of as a
full two-port VNA with the condition that both the ports can work as stimulus.
In the general case, the VNA is used to measure the reflection and transmission
coefficients of a device under test (DUT). For a one-port DUT, there is an incident
wave „a‟ that is generated by the VNA and supplied to the DUT and a reflected wave
„b’ returning to the VNA.
Fig. 4.1 Experimental Setup using Monostatic Radar
Table 4.1 Mono-static Radar Parameters
Parameters Specification
Operating frequency range 2 GHz to 4GHz
Radiated Power 10 dBm
No of frequency 201
Number of traces 1024
Antenna Gain 10 dBi
Antenna Beam width at centre frequency H-plane (34.560) and E-plane (35.94
0)
Antenna , VSWR 3:1
Circulator a) Insertion loss and Isolation loss 18 dB, 50 dB
For a one-port DUT, the incident and reflected waves are related by the reflection
coefficient which is defined by
a
b
(4.1)
If the normalized complex characteristic impedance z is defined by
oZ
Zz
(4.2)
where Z is the characteristics impedance of the system and Zo is arbitrary reference
impedance. Equation (4.1) can be rewritten as
1
1
z
z
(4.3)
This means that the ratio of reflected power to transmitted power can be completely
determined by the relationship between the complex impedance of the DUT and the
system reference impedance for a one port system. When this concept is extended to a
two-port DUT, a complete set of scattering parameter measurements is made by setting
the incident waves at Port 1 and then port two to zero and observing the system
responses in each case due to a stimulus at one port only. In the case of the VNA radar
system, there will be no stimulus at Port 2. The subscripts of the S-parameters denote
the port at which the response is observed, i and the port at which the stimulus is
presented, j, in the form Sij. This gives,
38 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
021
111 a
a
bS
(4.4)
and
021
221 a
a
bS
(4.5)
And, similarly, by considering there to be a perfect match at port one ( Γ1 = 0 )
and no incident stimulus ( a1 = 0 ) the reverse terms can be obtained as
02
2122 a
a
bS
(4.6)
and
02
1112 a
a
bS
(4.7)
Typical VNA measurements derived for magnitude & phase data are all S-parameters,
input and output impedance, Reflection Coefficient, Transmission Coefficient, Return
Loss, Voltage Standing Wave Ratio (VSWR), Group Delay, Phase Delay, Isolation
loss, insertion loss etc.
There are several key parameters for the VNA. This includes the number of
ports, power level, input power range, dynamic range, IF frequency, number of
frequency points, frequency range, averaging etc. The output power range for the VNA
simply specifies the minimum output, maximum output, and the step size of power
level available for the stimulus signal provided by the VNA to the DUT.
In case of radar, the system designer is able to determine the output power level
required to ensure a measurable return signal from the scene where there are lots of
discontinuities. Power levels in VNA are almost always specified in units of dBm by
convention.
4.2 Methodology 39
4.2.1.2 Horn Antenna
A horn antenna or microwave horn is an antenna that consists of a flaring
metal waveguide shaped like a horn to direct radio waves in a beam. A UWB pyramidal
horn antenna with 20 dB gain having bandwidth of 18 GHz is used for transmitting and
receiving signal. A coaxial cable feed line attaches to the connector visible at top. This
type is called as a ridged horn, the curving fins visible inside the mouth of horn
increases the antenna’s bandwidth.
The Half Power Beam width (HPBW) of antenna at centre frequency in H-
plane and E-plane are found to be 34.56о and 35.94
о respectively. The antenna is
oriented in vertical polarization for data collection. They are used as feed
antennas (called feed horns) for larger antenna structures such as parabolic antennas, as
standard calibration antennas to measure the gain of other antennas, and as directive
antennas for radar and microwave radiometers. Their advantages are
moderate directivity, low voltage standing wave ratio (VSWR), broad bandwidth, and
simple construction.
An advantage of horn antennas is that since they have no resonant elements,
they can operate over a wide range of frequencies, a wide bandwidth. The usable
bandwidth of horn antennas is typically of the order of 10:1, and can be up to 20:1 (for
example allowing it to operate from 1 GHz to 18 GHz). The input impedance is slowly
varying over this wide frequency range, allowing low voltage standing wave
ratio (VSWR) over the bandwidth. The gain of horn antennas ranges up to 25 dBi, with
10 - 20 dBi being typical. For the antenna which is used in the experiment, S11 is
measured from which return loss is obtained in the frequency range of 2 to 4 GHz.
The return loss R is given as,
)log(20 11SR (4.8)
The return losses are 18.84 dBm and 40.02 dBm at frequency 2.01 GHz and 3.78 GHz
respectively.
40 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
Fig. 4.2 UWB Ridge Horn Antenna
4.2.1.3 Circulator
A circulator is a passive non-reciprocal three- or four-port device, in which a
microwave or radio frequency signal entering any port is transmitted to the next port in
rotation only. A port in this context is a point where an external waveguide or
transmission line such as a coaxial cable connects to the device. For a three-port
circulator, a signal applied to port 1 only comes out of port 2; a signal applied to port 2
only comes out of port 3; a signal applied to port 3 only comes out of port 1. The
scattering matrix for an ideal three-port circulator is,
010
001
100
S
(4.9)
The important parameters of circulator are obtained by measurements. The insertion
loss is observed when port one is connected to input port of VNA, port 3 of circulator is
matched terminated and port 2 of circulator is connected to port two of VNA. The
insertion loss obtained for frequencies 2.91 GHz and 3.78 GHz are 38.75 dB and 18.48
dB respectively.
4.2 Methodology 41
Fig. 4.3 Connection with Circulator for calculation of insertion loss
The isolation loss is observed when port one is connected to input port of VNA, port 2
of circulator is matched terminated and port 3 of circulator is connected to port two of
VNA. It is desired that very negligible power to reach to port 3 of circulator. The
obtained values frequencies 2.11 GHz and 3.31 GHz are 48.15 dB and 53.39 dB.
4.2.2 Data collection
The data were acquired with a total number of 201 frequency steps. One sweep of this
entire band needs roughly 0.30 s to form one trace in time domain. For each data set,
we have collected at least 1024 traces. Data were collected using experimental system
as discussed earlier. The data collection is carried out in three stages. In the first stage
external calibration is done with the help of metal sheet. The second and third stages of
data collection are about change in type of wall. In the second stage plywood wall of 2
mm thickness is used and in third stage brick wall of 10 cm thickness is used. The
detail of data collected in Radar system given below.
4.2.2.1 Calibration using metal sheet with antenna
In this experiment, metal sheet of size 46 feet is kept in front of antenna at distance
0.5m and 1.5m. In this test scenario, we get all signal reflected back, from which we
can obtained the correction required due to connection of circulator
42 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
4.2.2.2 Data collection for plywood 2 mm wall
The experimental setup shown in Fig. 4.4 with single antenna and circulator is used for
data collection. The distance between antenna and plywood wall is varied as well as the
distance between plywood wall and target is varied. Table 4.2 shows the details of data
collection for different distances
Table 4.2 Data collected with various distance between Antenna and plywood wall and also
variation in distance between plywood wall and target.
Sr. No. Distance between
Antenna to wall (m)
Distance between Wall to
target (m)
Total distance including
plywood wall thickness (m)
1 0.5 1.5 2.02
2 1 1 2.02
3 1 1.5 2.52
4 1.5 0.5 2.02
5 1.5 1 2.52
4.2.2.3 Data collection for 10cm Brick wall
The data collection details for 10 cm brick wall are as given below in Table 4.3. Fig.
4.5 shows the experimental setup with antenna and circulator, the distance between
antenna and wall is 1m and wall to target is 0.5m
Table 4.3 Data collected with various distance between antenna and brick wall and also variation
in distance between brick wall and target
Sr.
No.
Distance between Antenna to
brick wall (m)
Distance between Brick Wall
to target (m)
Total distance including brick
wall thickness (m)
1 0.5 0.5 1.1
2 1 0.5 1.6
3 1.5 0.5 2.1
4.2 Methodology 43
Fig. 4.4 Experimental Setup with plywood wall
Fig. 4.5 Experimental Setup with brick wall
4.2.3 Signal Processing
The signal processing steps which are followed are shown in flowchart given in Fig.
4.6. The description of steps is as follow:
Step 1 Read trace data in Frequency domain
The data collected with the experimental setup is stored in frequency domain. Since
number of frequencies are 201 and number of traces are 1024, the size of matrix data
stored for one measurement is 201 by 2049. First column indicate frequencies and
second and third column indicates real and imaginary part for first trace, fourth and
44 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
fifth for second trace and so on. Hence the first step is to read the data for single trace
which is complex in form.
Step 2 Convert Frequency domain data into time domain data
Since information about location and presence of life sign is required,
processing of data is required to be done in time domain. So the data is converted in
time domain using inverse Fourier transform (IFFT).
Step 3 Convert time domain data into spatial domain data
Since location of target is required, time domain data is converted into spatial
domain. While converting the speed of wave through plywood wall and 10 cm thick
brick wall is considered same as speed of wave in air. For plywood this will not make
any significant difference in location of target that is standing behind it, but definitely it
will make difference for brick wall. That is the target will appear at location slightly far
away than actual as wave propagates through brick wall slowly.
Step 4 Detect locations by plotting all 1024 traces
After plotting the range profile for single trace different peaks due to
discontinuities in the scene are observed. These discontinuities are due to circulator,
antenna air interface, wall, target and multipath also. Since we want to detect the
location of target, and to find location, we need to detect the life sign such as
respiration and heart beat of target.
In a single trace, life sign cannot be detected since changes in phase and amplitude of
reflected signal is required to be detected. So all the traces are plotted and observed for
changes in amplitude at all distances. The distance where amplitude changes occur is
considered as location of target. Since there is displacement due to heart and respiration
of target, amplitude variations may occur at more than one location, provided the radar
range resolution is very good.
4.2 Methodology 45
Fig. 4.6 Flowcharts for Signal Processing
Step 5 Extract amplitude value from target location for all 1024 traces
Once the target location is obtained, the amplitude value at target location is
extracted from all the 1024 traces. Since amplitude variation is observed at more than
one place, three amplitude values are chosen, i.e., adjacent two values (previous and
later). The average of three values is taken and is normalized between plus one to
minus one.
Step 6 Apply FFT algorithm to extract Respiration frequency
46 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
Fast Fourier transform is applied on the normalized data obtained in previous
step. The sampling frequency should be chosen following Nyquist criteria.
4.3 Result and Discussion
The data collected as described earlier are used for processing. Data are collected in
three stages. In the first stage data are collected for external calibration. External
calibration is carried out to find out the how much correction is required due to used of
circulator in radar. Since the calibration is done before the point circulator is connected,
the reflection due to circulator will appear in the observations. In the second stage, data
are collected in the corridor where plywood wall is used and in third stage data is
collected in room where 10 cm thick brick-wall is used. The processing of data
collected in different stages is carried out and described below.
4.3.1 Metal Calibration
External calibration involves the measurement of returns from a calibrator with known
reflections, such as a metal plate. External calibration is necessary to characterize the radar
systemic error.
External calibration is carried out for finding external error in reading. In this
metal plate of dimension 34 m is kept in front of antenna. Two distances are taken i.e.,
0.5m and 1.5m. The results obtained are described in case I and II.
Case I: When distance between Antenna and Metal sheet is 0.5 m
It is observed from Fig. 4.7 (a) that, reflection due to metal is observed at 2.475
m. The difference between the actual and measured is 2.475-0.5=1.975m. So the
correction required due to circulator and antenna is 1.975m. . Also observed reflections
due to circulator and antenna air interface marked with text arrow in Fig. 4.7 (a).
Case II: When distance between Antenna and Metal sheet is 1.5 m
4.3 Result and Discussion 47
It is observed from Fig. 4.7 (b) that, reflection due to metal is observed at 3.45
m. The difference between the actual and measured is 3.45-1.5=1.95 m. So the
correction required due to circulator and antenna is 1.95 m. On average from case I and
case II results, 1.9625 m is the correction required.
Fig. 4.7 Metal calibration (a) When Antenna to Metal distance is 0.5m,
(b) When Antenna to Metal distance is 1.5m
4.3.2 Determination of location for data taken in corridor with
plywood
In this experiment, the subject is standing behind the plywood wall in normal breathing
condition. The distance between antenna and wall is varied from 0.5m to 1.5m and
distance between wall and subject (target) is varied from 0.5 m to 1.5m. The data
collection details are described in Section 4.2.2. The results obtained after processing
data for different cases are shown below.
Case I: When distance between Antenna and plywood Wall is 0.5m and distance
between Plywood wall to Human being is 1.5m
48 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
(a) Encircled part showing reflections (b) Location of target due to target
Fig. 4.8 Distance between Antenna and wall is 0.5m and between wall and target is 1.5m
Figure 4.8 (a) shows reflections due to target when all 1024 range profiles are plotted
All the 1024 traces are plotted on each other. The encircled area shows the reflection
due to target. The basic idea of detecting the human chest motion is by detecting the
time shift of peak of the reflected signal in subsequent signals which is directly
proportional to the displacement of the chest.
Here we have converted the time domain into range (distance) as shown in Fig. 4.8 (a).
It is identified as reflection due to target because, that portion appears shaded due to
variation in amplitude due to chest movement. Variation in amplitude occurs due to
respiration and heart beat of human being who is standing behind wall. The other
reflections in the Fig. 4.8 (a) appears with no variations in all 1024 range profiles which
indicate that they are from static parts present in between the antenna and human being
medium like circulator, wall, etc.
Figure 4.8 (b) shows the result obtained for the data taken when the distance
between antenna and wall is kept as 0.5m and distance between wall and target is 1.5m.
Since the plywood is of 2 mm thickness, the total distance should be 2.02 m. Figure 4.8
(b) shows location of reflections due to target when all 1024 range profiles are plotted
on each other. Figure 4.8 (b) shows the location of target as marked as 3.9 m. From
external calibration it is observed that 1.9625m distance should be subtracted from 3.9
m for obtaining correct location of target. So the distance obtained is 3.9m -1.9625m
4.3 Result and Discussion 49
=1.9375m. The error would be 2.021.9375 = 0.0825m which is acceptable as the
resolution is 0.075m. Figure 4.8 (b) also shows reflection due to circulator at location
1.725 m, antenna air interface at 2.1m and reflection due to plywood wall at 2.325m.
Since we are interested in reflection due to target, hereafter reflections due to antenna
air interface and reflection due to wall are not marked in figures.
Case II: When distance between Antenna and plywood Wall is 1m
(a) distance between Plywood wall to Human being is 1m
(b) distance between Plywood wall to Human being is 1.5m
Figure 4.9 shows reflections due to target when all 1024 range profiles are plotted on
each other. All the 1024 traces are plotted on each other.
Figure 4.9 (a) shows the result obtained for the data taken when the distance between
antenna and wall is kept as 1m and distance between wall and target is 1m. Since the
plywood is of 2 mm thickness, the total distance should be 2.02 m.
(a) Distance between wall to target is 1m (b) Distance between wall to target is 1.5m
Fig. 4.9 Distance between Antenna and wall is 1m
Figure 4.9 (a) shows the location of target as marked as 3.975 m. From external
calibration it is observed that 1.9625m distance should be subtracted from 3.975 m for
obtaining correct location of target. So the distance obtained is 3.975m1.9625m
=2.0125m. The error would be 2.0125 2.02=0.0075 m which is acceptable as the
resolution is 0.075m.
50 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
Figure 4.9 (b) shows the result obtained for the data taken when the distance
between antenna and wall is kept as 1m and distance between wall and target is 1.5m.
Since the plywood wall is of 2 mm thickness, the total distance should be 2.52 m.
Figure 4.9 (b) shows the location of target as marked as 4.425 m. So the distance
obtained after correction is 4.425 m 1.9625m =2.4625m. The error would be 0.0575 m
which is acceptable as the resolution is 0.075m.
Case III: When distance between Antenna and plywood Wall is 1.5m
(a) distance between Plywood wall to Human being is 0.5m
(b) distance between Plywood wall to Human being is 1m
(a) Distance between wall to target is 1.5m (b) Distance between wall to target is 0.5m
Fig. 4.10 Distance between Antenna and wall is 1.5m
Fig.4.10 shows location of reflections due to target when distance between antenna and
wall is 1.5 m and distance between wall and target is 0.5m. Figure 4.10 (b) shows
location of reflections due to target when distance between antenna and wall is 1.5 m
and distance between wall and target is 1m. Figure 4.10 (a) shows the location of target
as marked as 3.975 m. The error would be 0.0075m. Figure 4.10 (b) shows the result
obtained for the data taken when the distance between antenna and wall is kept as 1.5m
and distance between wall and target is 1m. Figure 4.10 (b) shows the location of target
as marked as 4.425 m. So the distance obtained after correction is 4.425 m 1.9625m
=2.4625m. The error would be 0.0575 m. The error values obtained from all the three
cases are shown in Table 4.4.
4.3 Result and Discussion 51
Table 4.4 Location determination when observation taken in corridor with Plywood Wall
Measured Observed Error
Distance
between
Antenna to
wall(m)
Distance
between
Wall to
Human
Being(m)
Total distance
including
plywood wall
thickness (m)
Reflection
due to
Circulator
(m)
Reflection
due to
Target (m)
Distance to
Target after
correction of
1.9625 m (m)
Difference
between
Observed and
measured
distance (m)
0.5 1.5 2.02 1.725 3.90 1.9375 0.08
1 1 2.02 1.725 3.975 2.0125 0.0075
1 1.5 2.52 1.725 4.425 2.4625 0.0575
1.5 0.5 2.02 1.725 3.975 2.0125 0.0075
1.5 1 2.52 1.725 4.425 2.4625 0.0575
4.3.3 Determination of frequencies of life sign for data taken in
corridor with plywood wall
Five data sets were processed using algorithm described in Section 4.2.3 to
extract the frequency of life sign signal. It is observed from results, respiratory
frequency is extracted but failed to extract heart beat frequency. It appears that heart
beat signal is harder to detect. If the results shown in Fig 4.11 (a) to (e) are observed,
there are many frequencies with insignificant amplitude values. These frequencies may
be due to harmonics and noise present in the signal. Figure 4.11 (a) is plotted when
distance between antenna and plywood Wall is 0.5m and distance between Plywood
wall to human being is 1.5m.
(a) (b)
52 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
(c) (d)
(e)
Fig. 4.11 Frequency Spectrum of human being standing behind plywood wall at different distance
Fig.4.11 (b) is plotted when distance between antenna and plywood Wall is 1m and
distance between Plywood wall to Human being is 1m. Fig. 4.11 (c) is plotted when
distance between antenna and plywood Wall is 1m and distance between Plywood wall
to Human being is 1.5m. Fig. 4.11 (d) is plotted when distance between antenna and
plywood Wall is 1.5m and distance between Plywood wall to Human being is 0.5m.
Fig. 4.11 (e) is plotted when distance between antenna and plywood Wall is 1.5m and
distance between Plywood wall to Human being is 1m. The breathing frequencies
measured for all the data set are tabulated in Table 4.5. The variation in breathing
frequency is from 0.1953 Hz to 0.332 Hz which is acceptable range.
Table 4.5 Frequency determination when observation taken in corridor with Plywood Wall
Distance between Antenna
to wall (m)
Distance between Wall to
Human Being (m)
Respiration Frequency
observed (Hz)
0.5 1.5 0.2734
4.3 Result and Discussion 53
1 1 0.1953
1 1.5 0.332
1.5 0.5 0.332
1.5 1 0.293
4.3.4 Determination of location for data taken in room with Brick
wall
In this experiment, the subject is standing behind the brick wall in normal breathing
condition. The distance between antenna and wall is varied from 0.5m to 1.5m and
distance between wall and subject (target) is kept constant that is 0.5 m. The data
collection details are described in Section 4.2.2. The results obtained after processing
data for different cases are shown below.
Case I: When distance between antenna and brick wall is 0.5m and distance
between brick wall to human being is 0.5m
Figure 4.12 shows the result obtained for the data taken when the distance between
antenna and wall is kept as 0.5m and distance between wall and target is 0.5m. Since
the brick wall is of 10 cm thick, the total distance should be 1.1m. Figure 4.12 (a)
shows reflections due to target when all 1024 range profiles are plotted on each other.
The encircled area shows the reflection due to target. The basic idea of detecting the
human chest motion is by detecting the time shift of peak of the reflected signal in
subsequent signals which is directly proportional to the displacement of the chest. It is
identified as reflection due to target because, that portion appears shaded due to
variation in amplitude due to chest movement.
54 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
(a)Encircled part showing amplitude variation (b) Target location
Fig. 4.12 Distance between antenna and Brick wall =0.5m
Variation in amplitude occurs due to respiration and heart beat of human being who is
standing behind wall. The other reflections in the Fig. 4.12 (a) appears with no
variations in all 1024 range profiles which indicate that they are from static parts
present in between the antenna and human being medium like circulator, wall, etc.
Figure 4.12 (b) shows location of reflections due to target when all 1024 range
profiles are plotted on each other. Figure 4.12 (b) also shows the location of target as
marked as 3.075 m. So the distance obtained after external calibration is 3.075m -
1.9625m = 1.1125m. The error would be 1.11251.1 = 0.0125m which is acceptable as
the resolution is 0.075m.
Case II: When distance between Brick wall to Human being is 0.5m
(a) distance between antenna and brick wall is 1 m
(b) distance between antenna and brick wall is 1.5 m
(a)When Distance between (b) When Distance between
Antenna and wall =1m Antenna and wall =1.5m
Fig. 4.13 Distance between Brick wall and Human target 0.5m
Figure 4.13 (a) shows location of reflections due to target when distance between
antenna and wall is 1m and distance between wall and target is 0.5m.
4.3 Result and Discussion 55
Figure 4.13 (b) shows location of reflections due to target when distance between
antenna and wall is 1.5m and distance between wall and target is 0.5m. Figure 4.13
(a) shows the location of target as marked as 3.75 m. From external calibration it is
observed that 1.9625m distance should be subtracted from 3.75 m for obtaining correct
location of target. So the distance obtained is 3.75m1.9625m =1.79m. Since the brick
wall is of 10cm thickness, the total distance should be 1.6m. The error would be 1.79-
1.6=0.19m which is acceptable as the resolution is 0.19m.
Figure 4.13(b) shows the result obtained for the data taken when the distance
between antenna and wall is kept as 1.5m and distance between wall and target is 0.5m.
Since the brick wall is of 10cm thickness, the total distance should be 2.1m. Fig.3.7 (b)
shows reflections due to target when all 1024 range profiles are plotted on each other.
All the 1024 traces are plotted on each other. Figure 3.7 (b) shows the location of target
as marked as 4.35 m. So the distance obtained after correction is 4.35 m 1.9625m
=2.39m. The error would be 0.29 m which is acceptable as the resolution is 0.29m. All
the error values obtained are tabulated as shown in Table 4.6.
Table 4.6 Location determination when observation taken in room with Brick Wall
Measured Observed Error
Distance
between
Antenna
to
wall(m)
Distance
between
Wall to
Human
Being(m)
Total
distance
including
Brick
wall
thickness
(m)
Reflection
due to
Circulator
(m)
Reflection
due to
Target
(m)
Distance
to Target
after
correction
of 1.96 m
(m)
Difference
between
Observed
and
measured
distance
(m)
Final
Distance
after
velocity
correction
Difference
between
Observed
and
measured
distance
after
velocity
correction
0.5 0.5 1.1 1.725 3.075 1.1125 0.0125 1.1 0
1 0.5 1.6 1.725 3.75 1.79 0.19 1.54 0.06
1.5 0.5 2.1 1.725 4.35 2.39 0.29 2.14 0.04
4.3.5 Determination of frequencies of life sign for data taken in room
with Brick wall
Three data sets were processed using algorithm described in Section 4.2.3 to
extract the frequency of life sign signal. After applying FFT to the amplitude variation
the highest peak is detected as a dominant frequency present in the spectrum. The
56 Detection of location and breathing signal of human standing behind brick
wall using monostatic radar system
respiration frequencies are noted in Table 4.7 for all data set. If the frequency spectrum
shown in Fig 4.14 (a) to (c) are observed, there are many frequencies with insignificant
amplitude values. These frequencies may be due to harmonics and noise present in the
signal. Figure 4.14 (a) is plotted when distance between antenna and brick Wall is 0.5m
and distance between brick wall to human being is 0.5m.
(a) Distance between antenna & brick wall is 0.5m (b) Distance between antenna & brick wall is 1m
(c) Distance between antenna and brick wall is 1.5 m
Fig. 4.14 Frequency Spectrum of human being standing behind plywood wall at different distance
Figure 4.14 (b) is plotted when distance between antenna and brick wall is 1m
and distance between brick wall to human being is 0.5m. Figure 4.14 (c) is plotted
4.4 Conclusion 57
when distance between antenna and brick wall is 1.5m and distance between brick wall
to human being is 0.5m.
Table 4.7 Respiration Frequency determination when observation taken in room with brick wall
Distance between Antenna to
wall (m)
Distance between Wall to
Human Being (m)
Respiration Frequency observed
(Hz)
0.5 0.5 0.273
1 0.5 0.356
1.5 0.5 0.332
4.4 Conclusion
In this chapter, we have proposed monostatic radar system to measure transmission
parameter with the help of circulator. The experimental results shows that proposed
system is useful when the distance between radar and target is small, since most of the
energy is consumed by circulator. Some clutter reduction technique can be used to
improve the signal strength so that detection of life sign signals is possible.
Since the human target is same for data collected when plywood wall and brick
wall is used, the respiration frequency values remains same as observed from results.
Also due to increase in distance between antenna and human being, the value of
respiration frequency does not changed.
Result shows successful detection of breathing frequency of human. In future,
need to work for detection of heart beat frequency using advance signal processing
technique.
Chapter 5 Detection of location of target
and breathing frequency of human being
standing behind brick wall using two antenna
systems
5.1 Experimental setup with 10 cm brick wall
To extract breathing signal of human subject hiding behind the brick wall,
measurement using an experimental setup is carried out in corridor of college building.
The experimental setup as shown in Fig. 5.1, consists of antennas, Vector Network
analyzer, Laptop, cables to connect them and human being standing behind brick wall.
The SFCW radar system parameters used in the experimental setup is described in
Table 5.1. Two identical pyramidal horn antennas with 20 dBi gain having bandwidth
of 18 GHz are used for transmission and reception. The distance between two antennas
is kept 0.30 m. The antennas are mounted on tripod with height adjusted such that the
antennas are exactly aligned with the chest of human being. The antenna is oriented in
vertical polarization for data collection. The dimension for brick wall is 5 6 feet with
thickness of 10 cm. Observations were carried out with and without target behind the
wall. The total distance between the antenna and target is varied in presence of wall.
5.2 Data collection with 10 cm thick brick wall
Three sets of data are collected. In the first data set, human being (target) is absent,
5.2 Data collection with 10 cm thick brick wall 59
whereas in second and third data set the target is present. Measurements were done on a
26 year old male test subject. The distance between wall and target (human being) is
varied whereas the distance between both antennas and wall is kept constant in all
measurements. In the second set, distance between antenna and target is 1m whereas in
third set, the distance between antenna and target is 1.5m.
Fig. 5.1 Experimental Setup with VNA, two antennas and human subject standing behind brick
wall
Two port standard calibration process i.e., Through Open Short Matched (TOSM) is
done for measurement of transmission parameter S21 in UWB frequency range i.e., 1
GHz to 3 GHz. Bandwidth of 2 GHz is used to achieve range resolution as 7.5 cm so
that reflection from even closely spaced targets can be achieved. The total numbers of
scans or observations that were carried out are 1024. The acquired data is saved in the
laptop for every measurement. Every measurement is organized as a matrix as shown in
Table 5.2, in which each row represents the number of frequency points i.e. N=201, i.e.,
signal for one frequency and column represents number of traces i.e., M=1024 i.e.,
number of times signal received at antenna location. For each trace, complex data in
frequency domain is stored.
60 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
Table 5.1 SFCW radar parameters with two antennas
Parameters Values
Frequency Range 1 to 3 GHz
Transmitted Power 10 dBm
Number of Frequency points 201
Number of Traces 1024
Antenna Type Horn
Gain of Antennas 20 dB
3 dB Beam width of Antennas 49.68(H-plane),
59.36 (E-plane)
5.3 Signal Processing Algorithm
To obtain the spectral content of the reflected signal from the human being due to chest
movement caused by respiration and heart beat, signal processing techniques are
applied. The signal processing is carried out in steps described in the flowchart, in Fig.
5.2, in the following subsection described the steps involved in flowchart.
Table 5.2 Matrix representing organization of data collected
Trace No.
Frequency. No.
1 2 3 4 … M
Fo S11 S12 S13 S14 … S1M
Fo+f S21 S22 S23 S24 … S2M
… … … … … … …
Fo+(N-1) f SN1 SN2 SN3 SN4 … SNM
Step 1. Read Trace data in Frequency domain
Signal is obtained by placing both (transmitting and receiving) antennas at a height that
is equal to height of chest of target and in line of sight of target. SFCW radar received
the data in frequency domain, and stored as shown in Table 5.2. To read the first trace,
column 1 data is picked up and filtered by applying a standard windowing function,
5.3 Signal Processing Algorithm 61
such as the Hamming window [17]. By applying hamming window, the side lobes are
reduced which will help to reduce false alarm rate and hence improve dynamic range of
the detection.
Step 2. Convert Frequency domain data to Time domain
After windowing in frequency domain, it is converted into time domain by using the
Inverse Fast Fourier Transform (IFFT). The converted signal is presented as signal
strength vs. time delay. The signal received at one of the measurement, after IFFT is
given by Freundorfer, et. al., [31],
)2exp()()(1
tfjfSts n
N
nn
(5.1)
where N is maximum number of frequency points, S(fn) is the received reflected
signal in frequency domain at nth
frequency and t varies from 0 to (N-1)/BW with step
interval of 1/BW, BW is bandwidth of the system.
Step 3. Convert Time domain data to spatial domain
The distance to targets is determined by constructing a range profile in the spatial
domain. The time domain signal is then converted to spatial domain and is called as
range profile. Range profile is one dimensional information and given by expression
as;
max1
0)/2(2exp()()( zzczfjfSzS n
N
nn
(5.2)
where z is down range distance given as
2/)( tcz (5.3)
and c is velocity of light.
62 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
The maximum distance Zmax and range resolution R are determined by following
formulas
BWNcZ 2/)1(max (5.4)
and
fNcR
2 (5.5)
where f is step size.
These are the basic steps that are implemented before processing the signal for further
analysis to retrieve information’s which are described in next section.
Step 4. Stacking all traces to form data matrix
A single range profile will be insufficient to give information about presence of life
sign of target. Radar must illuminate more number of traces. To see the amplitude
variations, all the 1024 traces are stacked one after another to form matrix.
Step 5. Apply Clutter reduction technique
The major problem in life sign detection of human being hiding behind brick wall is
loss of substantial amount of energy of signal due to reflection from wall. Due to high
contrast between brick wall and air, reflection occurs. Thus small amount of energy is
passed through wall and reach to the target. Again at target, due to dielectric contrast
between air and human being, reflections take place. The signal reflected by the target
reaches to receiving antenna after passing through wall again further reducing its signal
strength. As a result, this strong reflection which is called as clutter obscures the target
information. Also static clutter is caused due to contribution from other objects present
in external environment. Thus clutter reduction is inevitable for target detection. For
clutter reduction, it is important to separate target and clutter first from the received
5.3 Signal Processing Algorithm 63
data. Clutter reduction is carried out using the SVD technique. This subspace method
divides the data into target and clutter subspaces.
Let us consider that data are taken when the target is present and represented by the
matrix with dimensions N × M; and (i = 1, 2,…, N; j = 1, 2,…, M), where the indices i
and j are the distance (down range) and number of traces respectively. The SVD can be
represented as:
Step 1.Read Trace data in Frequency Domain
Step 2. Convert Frequency Domain Data into Time
Domain
Step 3.Convert Time Domain Data into Spatial Domain
Step 4.Stack all 1024 traces to Form data matrix
Step 5. Apply Clutter Reduction
Step 6.Find Location of target using SD
Repeat for all 1024 Trace Data
Step 7.Apply FFT to find frequency
64 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
Fig. 5.2 Flow-chart for processing steps
TUSVX (5.6)
Or
Tii
N
ii vuX
1
(5.7)
Or
NXXXX ...21 (5.8)
where Xi are called as ith
Eigen image of X. Our aim is to identify which Eigen image
represents target response. When range profile with and without presence of the target
is compared, we conclude that the second column of U (u2) i.e., second singular
component refers to the target signal. The target response (T1) estimated can then be
obtained from:
TvuXT22221
(5.9)
T1 represents the image having focused target response and reduced clutter. Since the
wall reflections are stronger than target reflections, the dominant first singular value
represents wall subspace. Also while taking measurement it is ensured that the antenna
is perpendicular to the wall surface i.e., antenna is not tilted at any angle with respect to
brick wall surface and also the wall thickness remains same for all the measurements
the clutter subspace does not span to multidimensional subspace [20]. However the
target subspaces do not remain single subspace and may span to multidimensional
subspace as there is variation of amplitude values at target location. So instead of
considering only second singular components as used in equation (5.9), all the singular
components related to target should be used. Since all N indices do not contribute to
target subspace, there is need to determine which singular components relates to target
subspace. The distance from range profiles are used to find out the corresponding
singular components. The target subspace obtained using multiple Singular components
5.3 Signal Processing Algorithm 65
are obtained as equation (5.10);
i
Tiii vuT2
(5.10)
where is a set of all indices for target singular vectors.
More details about the clutter reduction technique are described in [17].
Step 6. Find Location of target using standard deviation (SD)
The purpose of processing should be to help the radar operator to clearly understand
whether life sign of target is present or absent. The amplitude value in all the range
profiles at different distances should be observed. If there is no amplitude variation at
all the distances in all 1024 traces, then target is absent. If target is present, then at
target location there will be amplitude variations at all 1024 traces due to chest
movement caused by respiration and heart beat. To observe whether there is amplitude
variation or not, standard deviation (SD) is calculated at all the distances over all the
range profiles using equation (5.11).
1
0
)(1 M
mid x
MSD
(5.11)
where d is distance taken upto 5 m with step size of 0.075 m and M is total number of
traces. The value of SD at human being location will be higher compared to static
objects.
Step 7. Extraction of Breathing Frequency using the Fast Fourier Transform Method
Once the location where SD value is maximum is obtained, the following steps are
applied before the Fast Fourier transform (FFT) method is used for extraction of life
sign.
1. Extract amplitude of signal from all traces for the location obtained in step 6. Since
there is fluctuation, instead of amplitude at one location, succeeding and preceding
locations amplitude are also picked up and their average is taken as amplitude
66 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
variations at target location. Since there are 1024 traces, total number of amplitude
values will be 1024.
2. Normalize the amplitude values between 1.
3. Convert the extracted signal in step (ii) (amplitude versus number of traces) into
frequency domain by applying the FFT algorithm.
4. The breathing frequency for normal human being corresponds to frequencies 0.2 to
0.5 Hz while heart rate corresponds to 0.8 to 1.5 Hz. Since we are interested in
frequencies in the range of 0.2 to 1.5 Hz, a second order band pass filter using butter
worth is applied before the FFT.
5.4 Result and Discussion
The aim is to develop algorithm to improve the detection of respiratory motion
response of a human being standing behind the brick wall by reducing stationary clutter
originating from fixed object like wall reflections, reflections due to antenna air
interface, multipath etc.,. The performance of algorithm is investigated by carrying out
experimental work in absence of target and by changing distance between source and
human target. Three sets of data are collected by the experimental work. In the first set
the target is absent, in second set the distance between radar and standing human being
is set as 1m and in third set it is 1.5m. The processing is done according to the
flowchart described in Fig. 5.2. Results for these sets of data are discussed below.
5.4.1 Absence of target behind brick wall
First set of data is considered for processing which is taken without target. Figure 5.3
show range profiles, when all the 1024 traces overlapped on each other. The figure is
plotted for distance versus magnitude. For range profile, maximum distance calculated
using equation 5.4 is 15 m but it is taken up to 5 m.
5.4 Result and Discussion 67
When the target is absent behind brick wall only three significant peaks are observed as
shown in Fig. 5.3. The first peak is due to weak isolation between transmitting and
receiving antenna, second peak is due to front side of brick wall and third peak is due to
back side of brick wall. Since the radar is used in UWB range, it resolve front and back
side of brick wall as thickness of wall is greater than range resolution. As target (human
being) is absent, amplitude variation in range profiles is not observed. The remaining
small peaks observed thereafter are due to the multipath reflections.
5.4.2 Presence of target behind Brick wall
Detection of target can be obtained from the single range profile by observing reflected
amplitude at target location. To observe amplitude variations due to
breathing/respiration, radar must illuminate more number of traces. To see the
amplitude variations all the range profiles are stacked one over other. Thus the
information in more than one traces are collected. Figure 5.4 (a) shows the range
profiles for all 1024 traces when the second set of data is used in which target (human
being) is present behind the wall. When the target is present, additional peaks compared
to Fig. 3, due to reflection from target (human being) is observed as shown in Fig.5.4
(a) and (b). The shaded portion represents presence of target marked by data tip, which
indicate location as 1.35m and 1.8m for second data set and third set respectively. It is
noted that, location of the target from the antenna system is different from actual due to
presence of brick wall in between them.
The amplitude values for detected peaks i.e. peak due to antenna-air interface, due to
wall surface and due to human target for all 1024 traces are observed from Fig. 5.4 (a)
and (b). From the Fig. 5.4, it is clearly observed that for the first three peaks i.e. peak
due to antenna-air interface and peak due to front and back side of wall surface, there is
no variation in amplitude for all 1024 traces, while, there is significant amplitude
variation at human target location. The variations in amplitude at locations of target are
due to respiration and heart rate. Since the range resolution is 0.075m, the minimum
difference between two adjacent locations will be 0.075 m. We can observe from Fig.
5.4 (a) and (b), that the amplitude of the clutter (i.e., reflection due to antenna air
68 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
interface and wall) is higher than reflection due to human being. By using clutter
reduction technique, improvement in the target signal strength can be achieved which is
described in next section.
5.4.3 Raw Image (Amplitude versus Slow time variation)
All the range profiles acquired are stacked one after another so that two dimensional
image is obtained in which on x-axis we have number of range profiles or number of
traces and on y-axis we have down range distance. This set of traces, can thus be
assembled together in a two dimensional structure, and visualized as an image known
as raw image [18]. The raw image as shown in Fig.5.5 (a) and (b) for second and third
data set is obtained. We can observe that the amplitudes of the clutter (i.e., reflection
due to antenna air interface and wall reflections) are higher than target reflection. i.e.,
though target is present, the signal strength is so weak that the reflection due to target is
not significant in Fig.5.5 (a) and (b). The improvement in the target signal strength can
be achieved by using clutter reduction technique which is described in next section.
5.4.4 Improvement in the detection using Clutter reduction technique
The clutter reduction as described in step 5 of Fig. 5.2, is applied on raw image. In
Fig.5.6 (a) and (b), the changes in the amplitude at target location due to human subject
movements can be easily observed i.e., from Fig. 5.6 (a), at 1.35 m in range, there is
amplitude variation as observed with dark and light shaded portion all along the number
of traces.
5.4 Result and Discussion 69
Fig. 5.3 Range profiles for all 1024 traces in absence of target
(a)Second data set
70 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
(b) Third data set
Fig. 5.4 Range profiles for all 1024 traces in the presence of target
Similarly from Fig. 5.6 (b), at 1.8m, the amplitude variations are observed. From
these results one can visualize that target is enhanced with suppression of clutter.
(a) Second data set
(b) Third data set
5.4 Result and Discussion 71
Fig. 5.5 Raw Image
To measure the performance of clutter reduction technique, average target signal
strength is calculated. For second data set, the average signal strength value before
clutter reduction at target location is 0.045 whereas after clutter reduction using only
second singular components (using equation 9) it is 0.2732. It is further improved when
seven singular components related to target subspace are used to form target image as
described in equation (10) to 0.3777. These are obtained by observing range profiles for
each singular component. If the target reflection is observed then that singular
component is considered for forming target image. All such singular components are
used to form final target image. The image formed after adding all images
corresponding to seven singular components i.e., second, third, fourth, fifth, sixth,
seventh and eigth, is obtained as shown in Fig.5. 7(a). For the third data set, the
average signal strength is 0.0055 before clutter reduction and after clutter reduction
using only second singular component, it is improved to 0.3950. When the three
singular components i.e., second, third and fourth are used the average signal strength is
further increased to 0.4436. The image formed after adding all images corresponding to
three singular components i.e., second, third and fourth, is obtained as shown in Fig.
5.7(b). Here since the distance between radar and target is increased, the number of
singular components related to target subspace has reduced. In Fig.5.7 (a) and (b), the
changes in the amplitude at target location due to human subject movements can be
easily observed at same location as earlier. Thus enhanced value of target signal
strength at target location is observed either using single singular component or
multiple singular components related to target space are taken.
72 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
(a) Second data set
(b) Third data set
Fig. 5.6 Image after clutter reduction
(a) Second data set
5.4 Result and Discussion 73
(b) Third data set
Fig. 5.7 Image formed using Target Singular Components
5.4.5 Location Determination using standard deviation
To obtain the location of human being, SD is calculated at all distance value using
equation (5.11). The location at which maximum value of SD is obtained is verified
with the location of human being set during experimental setup. The Table 5.3, gives
SD values for both the experimenntal data sets. The location at which SD is highest is
obtained by using index value. From Table 5.3, for the second data set, the index value
obtained is 18 which give distance as 1.35m. Index value is converted into distance by
multiplying it by range resolution of 0.075. Similarly for third data set, the index value
is 25 and the distance obtained is 1.8m. If we compare the result obtained in Table 5.3
and result obtained in Fig. 5.4, the target locations in both cases are same. Thus it
confirms that SD can be used to find location and presence of target.
Table 5.3 Location of target obtained using SD
Maximum SD value Index /location at
which maximum SD
occurs
True Location of
target after
correction (m)
Second Set of
Experiment
0.0253/0.0270 18/1.35m 1.05
Third Set of
Experiment
0.0112/0.0120 25/1.8m 1.5
74 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
In the second data set, the actual distance between antennas and target is 1m but
distance obtained is 1.35 m marked by data tip as shown in Fig. 5.4 (a). The true
location can be obtained by using equation (5.12).
)1( rwallobsevedtrue ddd (5.12)
where dobserved is observed distance from Fig. 4 (a) which is 1.35m, dwall is thickness of
wall which is 0.1m and r is average dielectric constant which is measured as 9 of the
wall. After using external metal calibration, which gives shift of 0.1m, the calculated
true location has 5cm error for the second data set whereas for third data set the error is
zero. Since the resolution of radar system is 0.075 m the error is within acceptable
range.
5.4.6 FFT based Results
After finding location of target, the steps described in step 6 of Fig. 5.2., are applied. It is
observed that the adjacent position also have significant amplitude variations. So, three
amplitude values are selected i.e., the location at which SD is maximum, previous and
succeding location. The average of three locations amplitude is taken as input to FFT.
Assuming normal breathing frequency range as 0.2-0.5 Hz for human, a band pass filtered is
used . The result obtained after FFT is shown in Fig. 5.8 (a) for second data set and in Fig.
5.8(b) for third data set. The frequency components of interest are chosen by observing largest
amplitude. The frequencies observed are 0.4102 Hz and 0.3125 Hz respectively. Similar results
are obtained if image after clutter reduction using all target subspace is used. So the breaths/min
would be at about 24 and 19 respectively. From Fig. 5.8, other smaller peaks are observed
which are probably due to harmonics of breathing frequencies or clutter.
5.5 CONCLUSION 75
(a) Second data set
(b) Third data set
Fig. 5.8 Frequency Spectrum
5.5 CONCLUSION
The focus of this chapter is to explore the possibility of improvement in the detection and
extraction of breathing frequency of human being positioned behind the brick wall using SFCW
radar system in UWB frequency range. After applying clutter reduction technique using SVD,
clutters are successfully minimized which implies that the technique is powerful. Target signal
strength is improved further by using all Singular components related to target. Due to use of
clutter reduction target signal strength increases which increases the probability of correct
target detection.
76 Detection of location of target and breathing frequency of human being
standing behind brick wall using two antenna systems
Increase in target signal strength is useful for process of extraction of life sign signal.
The proposed methods not only extract the breathing, but also estimate the location of
human being using SD. The determination of higher SD value helps in the automatic
detection of presence of life sign signal of human being along with location. The
experimental results also show that FFT method is useful for extraction of breathing
frequency of human being after using band pass filter. From result obtained using
second and third set data, it is observed that when the distance between antenna and
target is increased, the amplitude of breathing signal reduces.
Extraction of heart beat frequency can be carried out in future using advance signal
processing techniques. The effect of non stationary clutter i.e., other movements in
nearby region of the detection is not considered in this work. In future study, brick wall
can be replaced by different complex type of walls like concrete with metal inside can
be considered and its effect on the detection can be studied. Real time analysis for the
detection and extraction of life sign frequencies is one of the major interests of users.
The developed techniques may be explored to be applied for real time analysis.
6.1 Experimental Setup 77
Chapter 6 Effect of Thickness of wall on
detection of location and breathing frequency of
human being standing behind the brick wall
The purpose of this chapter is to see the effect of thickness of brick wall on detection of
life sign. Two experiments were carried out, one with 0.32 m thick brick wall and
second with 0.22 m thick brick wall.
Swath calculations
6.1 Experimental Setup
The experiment was carried out with bi-static antenna mode as shown in Fig.6.1 where
vector network analyzer, cables, and laptop were used. Standard two port calibration
processes i.e., Open Short Matched and Through was done to calibrate VNA before
transmission parameter S21 is measured in frequency range from 1 GHz to 3 GHz. Total
1024 numbers of scans or observations were carried out. Two identical pyramidal horn
antennas with 10 dB gain having bandwidth of 2 GHz were used for transmission and
reception. The antennas were mounted on tripod with height adjusted such that the
antennas are exactly aligned with the chest of human being with 50 cm distance
between two antennas and facing same direction. The antennas were oriented in vertical
polarization for data collection. The experiments were carried out with a brick wall
having thickness of 32cm. In performing the measurements, transmit and receive
antennas were kept at fixed locations and aligned for maximum signal reception. The
78 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
distance between antennas should be sufficiently large such that the wall is in the far
field of antenna. During the experiments, the antennas were placed at different
distances from the brick wall. Data is collected by using software VEE pro controlled
by PC and VBA macro program and transferred to PC for further analysis. MATLAB
software use is for data processing.
6.2 Data collection
6.2.1 When thickness of brick wall is 0.32 m
Measurement was done for 0.32 m thick brick wall keeping distance between antennas
and brick wall as fixed whereas distance between wall and human target is varied.
Table 6.1 describes three different cases utilized for data collection.
In case 1, distance between Antenna and wall is 0.5m and distance between wall and
human target is varied from 0.5 m to 1.5m. In case 2, distance between Antenna and
wall is 1m and distance between wall and human target is varied from 0.5 m to 2 m
whereas in case 3, distance between Antenna and wall is 1.5m and distance between
wall and human target is varied from 0.5 m to 2m.
6.2 Data collection 79
Fig. 6.1 Experimental Setup with two antenna system
Table 6.1 Data Collection details for 0.32 m Brick wall taken with two antenna system
Sr. no. Distance between
Antenna and Wall (m)
Distance between Wall
and Target (m)
Total Distance including
Wall Thickness (m)
Case 1
0.5 0.5 1.32
0.5 1 1.82
0.5 1.5 2.32
0.5 2 2.82
Case 2
1 0.5 1.82
1 1 2.32
1 1.5 2.82
1 2 3.32
Case 3
1.5 0.5 2.32
1.5 1 2.8
1.5 1.5 3.32
1.5 2 3.82
6.2.2 When thickness of brick wall is 0.22 m
Similar measurements for 0.22 m thick brick wall were carried out as done for 0.32 m
thick brick wall. Table 6.2 describes three different cases utilized for data collection.
Table 6.2. Data Collection details for 0.22 m Brick wall taken with two antenna system
Sr. no. Distance between
Antenna and Wall (m)
Distance between Wall
and Target (m)
Total Distance including
Wall Thickness (m)
Case 4 0.5
0.5 1.22
1 1.72
2 2.72
Case 5 1
0.5 1.72
1 2.22
1.5 2.72
Case 6 1.5
0.5 2.22
1 2.72
2 3.72
In case 4, distance between Antenna and wall is 0.5m and distance between wall and
target is varied from 0.5 m to 2m. In case 5, distance between Antenna and wall is 1m
and distance between wall and target is varied from 0.5 m to 1.5 m whereas in case 6,
distance between Antenna and wall is 1.5m and distance between wall and target is
varied from 0.5 m to 2m.
6.3 Signal processing technique
Processing steps given in the flow chart as shown in Fig. 6.2 are applied to extract
signal representing life sign. IFFT is used to convert data collected in frequency domain
to time domain. To locate the target and extract range information, time domain data is
converted into spatial domain using the relation between time, velocity and distance.
Once the signal is in spatial domain, the location of target is obtained. Thus the location
along with the amplitudes can be obtained from range profile. Aim is to detect the chest
movement of human being. Since single trace won’t give the location of chest
movement, all 1024 traces were used. The procedure is repeated to obtained amplitude
variation at location of human being (target). All the 1024 range profiles obtained are
stack one over other so that amplitude variation can be observed as given in step
4.Amplitude variation indicates presence of human target as obtained in step 5. Once
the location of amplitude variation is obtained, then the amplitude value at that location
is extracted. This is done for all the 1024 traces i.e., we formed the matrix of size
1x1024. Instead of taking amplitude of one location, averages of three locations i.e.
succeeding and preceding the target locations amplitude values are taken as given in
6.4 Result and Discussion 81
step 6. Due to chest movement, the location of amplitude may vary. The variation in
location depends on the range resolution which is in this case is 0.075 m. Now
according to step number 7, apply FFT algorithm to find frequency of breathing signal.
Step 1.Read Trace data in Frequency Domain
Step 2. Convert Frequency Domain Data into Time
Domain
Step 3.Convert Time Domain Data into Spatial Domain
Step 4.Stack all 1024 traces one above another to
observe amplitude variation
Step 5.Note the Location of amplitude variation as target
location
Step 6. Take the average of three amplitude values for all
1024 traces
Repeat for all 1024 Trace Data
Step 7.Apply FFT to find frequency
Fig. 6.2 Flow chart of Signal processing technique
82 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
6.4 Result and Discussion
6.4.1 External calibration
External calibration is carried out with the help of metal. The experimental setup
is as shown in Fig. 6.3 in which metal is kept at a distance of 1m from antenna.
After processing the data collected for external calibration, we observed the
results as shown in Fig. 6.4, in which the first peak is obtain because of reflection
due to antenna coupling at a distance of 0.45m and second peak is obtained at 1.2
m, due to reflection from metal sheet placed in front of antennas. Thus there is
need of correction of 0.2m.
Fig. 6.3 External calibration using Metal sheet
6.4 Result and Discussion 83
Fig. 6.4 Range profile for external calibration
6.4.2 When thickness of brick wall is 0.32 m
6.4.2.1 Detection of position of different peaks with amplitude
We have processed the data according to the algorithm as described in section 6.2. Data
collections as given in Table 6.1 and 6.2 are used for processing. The processing of data
is carried out in steps i.e., case 1 to case 3 data is used for analysis and are described
below.
Case 1: When distance between Antenna and Brick-wall is 0.5m
The case 1 data as described in Table 6.1 is taken for analysis. The measurements were
taken when the distance between antennas and brick wall is at a fixed distance of 0.5m
and the distance of target from the wall is varied as 0.5 m, 1 m, 1.5 m, and 2 m. Figure
6.5 show the result obtained when the distance between target and wall is 0.5 m. From
Fig. 6.5, the first peak is due to reflection from antenna, second peak is due to reflection
from front side of brick wall, third peak is due to reflection from back side of brick wall
and the fourth is from target.
When the target is at distance of 0.5 m from wall the actual distance from antenna to
target is 1.32m and observed distance from Fig. 6.5 is 1.95m which is also shown in
84 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
Table 6.3. In Fig. 6.5 the first peak due to reflection from antenna is at position 0.45m
whose amplitude is of 0.2463. Second peak is due to reflection from front side of brick
wall is at position 0.75m whose amplitude is 1. The third smaller peak is due to
multipath and is at 1.2m whereas the back side of brick wall is observed at position
1.45m whose amplitude is 0.1187. Next peak is due to the target position at 1.95m
whose amplitude is 0.03959. Table 6.3 shows location and amplitude of various peaks,
in which sr. no. 1 shows the result when target is at the 0.5m from brick wall, sr. no. 2
shows the result when target is at 1m from brick wall, sr. no. 3 is when target is at 1.5m
from brick wall and sr. no. 4 is when target is at 2m from brick wall. It is observed from
the results obtained in Table 6.3 that as distance between antennas and target increases,
the amplitude at target reduces. Also there are other peaks apart from peaks due to
objects present in scene of measurements. The reasons for the presence of this peak
may be due to multipath.
Fig. 6.5 For case 1:Distance between wall and human target is 0.5m
Table 6.3 Position and amplitude when distance between Antenna and Brick-wall is 0.5m
Sr.
No.
Distance
between
First peak due
to antenna
Second peak
due To front side
Third peak due
to back side of
Fourth peak
due to target
6.4 Result and Discussion 85
Case 2: When distance between Antenna and Brick-wall is 1m
Measurements are taken when the antenna is at a fixed distance of 1m from wall and
the distance (position) of target is changed from the wall as 0.5m, 1m, 1.5m and 2m.
When the target is at distance of 0.5 m from wall the actual measurement from antenna
to target is 1.82 m and observed measurement from Fig. 6.6 is 2.475m and also shown
in Table 6.4. In Fig. 6.6, the first peak due to reflection from antenna is at position
0.45m whose amplitude is of 0.427m. Second significant peak due to reflection from
front side of brick wall is at position 1.2 m whose amplitude is 1. Third peak is due to
back side of brick wall is at position 1.4m whose amplitude is 0.04694. Fourth peak is
due to the target is at position 2.475m whose amplitude is 0.06259. Table 6.4 shows
position and amplitude of various peaks, for the remaining data. It is observed from the
comparison of results obtained from case 1 and case 2, that the number of reflections
increases due to multi paths when the distance between antennas and target increases.
Case 3: When distance between Antenna and Brick-wall is 1.5m
Measurements are taken when antennas are at a fixed distance of 1.5 m from brick wall
and the position of target from the wall are changed from 0.5 m to 2 m in step of 0.5 m.
The results obtained after processing are tabulated in Table 6.5. In below Fig. 6.7, the
encircled peak indicates reflection due to target. The position and amplitude is given in
Table 6.5. Table 6.5 shows position and amplitude of various peak, in which sr. no. 1
shows the result when target is at the 0.5m from brick wall, sr. no. 2 shows the result
when target is at 1m from brick wall, sr. no. 3 is when target is at 1.5m from brick wall
and sr. no. 4 is when target is at 2m from brick wall. It is observed from results shown
in Table 6.5, that as the distance between antennas and target increases, detection of
target peak becomes difficult due to very low amplitude value.
wall and
human
target
of brick wall brick wall
Position Amp. Position Amp. Position Amp. Position Amp.
1. 0.5m 0.45m 0.2463 0.75m 1 1.425m 0.1187 1.95 m 0.03959
2. 1 m 0.45m 0.2468 0.75m 1 1.425m 0.1194 2.4 m 0.02
3. 1.5m 0.45m 0.247 0.75m 1 1.425m 0.1193 2.85 m 0.0147
4. 2 0.45m 0.2467 0.75m 1 1.425m 0.1189 3.45 m 0.00456
86 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
Table 6.4 When distance between Antenna and Brick-wall is 1m
Fig. 6.6 For case 2:Distance between wall and human target is 0.5m
Sr.
No.
Distance
between
wall and
human
target (m)
First peak due
to antenna
Second peak
due To front
side of brick
wall
Third peak due
to back side of
brick wall
Fourth peak
due to target
Position Amp. Position Amp. Position Amp. Position Amp.
1. 0.5
0.45m 0.427 1.2m 1 1.875m 0.04694 2.475 m 0.06259
2. 1 0.45m 0.4196 1.2m 1 1.875m 0.04741 2.85 m 0.01715
3. 1.5 0.45m 0.4206 1.2m 1 1.875m 0.04664 3.675 m 0.01113
4. 2 0.45m 0.4212 1.2m 1 1.875m 0.04775 3.975 m 0.0099
6.4 Result and Discussion 87
Fig. 6.7 For case 3:Distance between wall and human target is 0.5m
Table 6.5 When distance between Antenna and Brick-wall is 1.5m
When the results obtained in Table 6.4 and 6.5 are compared for same distance between
Antenna and human target i.e., for example sr. no. 4 from Table 6.4 and sr. no. 3 from
Table 6.5, the distance between Antenna and human target is 3m but the amplitude
value at location of target is different. This reduction in amplitude is due to changes in
swath as the distance between antenna and wall changes. From Table 6.4, the distance
between Antenna and wall is 1m whereas in Table 6.5, the distance between them is
1.5m. As the swath increases, intensity reduces.
Sr.
No
.
Distanc
e
between
wall
and
human
target
(m)
First peak due to
antenna
Second peak
due To front
side of brick
wall
Third peak due to
back side of brick
wall
Fourth peak
due to target
Positio
n Amp.
Positio
n
Amp
.
Positio
n Amp.
Positio
n Amp.
1. 0.5 0.45m 0.00508
3 1.725m 1 2.1m
0.00075
4 2.925m 0.000117
2. 1 0.45m 0.5673 1.725m 1 2.4m 0.06354 3.30m 0.01865
3. 1.5 0.45m 0.5648 1.725m 1 2.4m 0.06227 3.825 0.02117
4. 2 0.45m 0.00505
7 1.725m 1 2.1m
0.00074
6 4.35m
0.000150
5
88 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
6.4.2.2 Detection of location of Human being
From flowchart given in Fig.6.2, after observing range profile the distance to target is
noted down as shown in Table 6.6. It is observed from the results that there is
difference between the actual distance between antennas and target and measured
distance at target. The difference is given in last column of Table 6.6. One of the
reasons for difference is due to change in velocity of wave when it propagates through
different medium other than air.
6.4.2.3 Detection of location of Human being after Velocity correction
For example, from Table 6.6, from case 1, if first row is considered, the distance
between antenna and wall is 0.5m and distance between wall and target is 0.5 m. The
total distance between antennas and target by considering wall thickness as 0.32 m will
be 1.32 m. But the observed result is 1.95 m. So the difference is of 0.63 m. Due to
external calibration there is correction of 0.2 m which gives the distance as 1.75m
instead of 1.95m. Hence the difference of 1.75-1.32=0.43 m shows the deviation of
actual location because of decrease in velocity of microwave when the wave propagates
through brick wall. This effect can be minimized by using velocity correction. The
corrected location of human target related to antenna can be calculated by equation
(6.1),
Distance after Correction = Observed distance D ( )1( (6.1)
Where, D is the thickness of brick wall which is 0.32m. Assume as 4.5.
Distance after Correction =1.75 - 0.32(*1.1213)
= 1.75 - (0.3588)
=1.39 m
The distance after correction matches with the actual measurement used for data
collection, thus the error is 0.0004m which falls in acceptable range as range resolution
6.4 Result and Discussion 89
is 0.075 m. For the remaining data using (6.1), the actual/measured location is
compared with velocity corrected location and difference (error) are calculated which
are shown in Table 6.6. The values of error are acceptable. The Table 6.6 also gives the
maximum values of standard deviation (SD) on the basis of which the location of
human target is obtained.
Table 6.6 Location obtained from Experimental data after velocity correction
Sr no. Brick
wall
thicknes
s
(m)
Distanc
e
betwee
n
Antenn
a and
wall
(m)
Distanc
e
betwee
n wall
and
target
(m)
Measur
ed
Total
distanc
e (m)
Distance
to target
observe
d from
result
(m)
Actual
Distance
after
correction
external and
velocity (m)
(0.4304+0.2
)=0.6304
Differenc
e
Between
Measure
d and
observed
distance
Maximum
Sigma
Value
observed
at human
target
location
Case 1
0.32 0.5 0.5 1.32 1.95 1.3196 0.0004 0.0039
0.32 0.5 1 1.82 2.4 1.7696 0.0504 0.0014
0.32 0.5 1.5 2.32 2.925 2.2946 0.0254 0.0011
0.32 0.5 2 2.82 3.45 2.8194 0.0004 0.00072
Case 2
0.32 1 0.5 1.82 2.475 1.8446 0.0246 0.0011
0.32 1 1 2.32 2.85 2.2196 0.1004 0.0013
0.32 1 1.5 2.82 3.45 2.8196 0.0004 0.0015
0.32 1 2 3.32 4.05 3.4196 0.0999 0.0007
Case 3
0.32 1.5 0.5 2.32 2.925 2.2946 0.0254 0.0036
0.32 1.5 1 2.82 3.485 2.816 0.004 0.0019
0.32 1.5 1.5 3.32 3.9 3.6446 0.054 0.0045
0.32 1.5 2 3.82 4.5 3.8696 0.0496 0.0048
6.4.2.4 Extraction of Amplitude variation from target location
For better understanding, the amplitude values for detected peak i.e., peak due to
antenna coupling, due to front side of wall, due to back side of wall and due to human
target for all 1024 traces were recorded. It is clearly observed that for the first three
peak i.e., peak due to antenna coupling, due to front side of wall, and due to back side
of wall there is no variation in amplitude for all 1024 traces. There is significant
amplitude variation at human target location. So in the next step only amplitude
variation portion is extracted.
90 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
Instead of extracting amplitude from single location, succeeding and preceding location
i.e., total three location amplitude value is taken and there average is calculated for all
1024 traces. This average value at all 1024 traces is taken and a plotted. The time
domain plots are obtained for all the cases i.e., case 1, case 2 and case 3 data sets. The
average amplitude variation plot at target location is shown in Fig. 6.8 for different
distances between wall and target for case 1 data set. Similarly for case 2 and case 3
data set, the average amplitude variations at target location is plotted in Fig. 6.9 and
Fig. 6.10 respectively.
a. Average when target is at 0.5m b. Average when target is at 1m
c. Average when target is at 1.5m d. Average when target is at 2m.
6.4 Result and Discussion 91
Fig. 6.8 Plot of amplitude variation for case 1 data
a. Average when target is at 0.5m b. Average when target is at 1m
c. Average when target is at 1.5m d. Average when target is at 2m
Fig. 6.9 Plot of amplitude variation for case 2 data
The plots shows that there are multiple frequencies present in the signal i.e., it includes
breathing signal, heartbeat signal and some clutter. It is also observed from the
amplitude variations that The reason of presence of different signals may be due to
presence of brick wall in between radar antennas and human target. Thus it makes
extracting of breathing frequency more complex.
92 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
a. Average when at 0.5m b. Average when target is at 1m
c. Average when target is at 1.5m d. Average when target is at 2m
Fig. 6.10 Plot of amplitude variation for case 3 data
6.4.2.5 Detect breathing frequency using FFT
After applying the FFT to the amplitude variations, the results are obtained and are
rightly shown in Fig. 6.11 for case 1 dataset, in Fig. 6.12 for case 2 dataset and Fig.
6.13 for case 3 data set. In frequency spectrum, the peak is detected and corresponding
breathing frequency (0.2-0.7 Hz) for human are noted as shown in Table 6.7. In this
experimental study heartbeat frequency (1.2-1.7 Hz) was not detected due to its
6.4 Result and Discussion 93
relatively weak amplitude variation. It is observed from results, that there are multiple
frequencies present which are due to harmonics and clutter.
a.when target is at 0.5m from wall b. when target is at 1m from wall
c. when target is at 1.5m from wall d. when target is at 2m from wall
Fig. 6.11 Breathing frequency extraction using FFT for case 1 dataset
94 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
a. when target is at 0.5m from wall b. when target is at 1m from wall
c. when target is at 1.5m from wall d. when target is at 2m from wall
Fig. 6.12 Breathing frequency extraction using FFT for case 2 dataset
a when target is at 0.5m from wall b when target is at 1m from wall
6.4 Result and Discussion 95
c when target is at 1.5m from wall d when target is at 2m from wall
Fig. 6.13 Breathing frequency extraction using FFT for case 3 dataset
Table 6.7 Breathing frequency values for different datasets
Case 1 Case 2 Case 3
0.4492 Hz 0.5078 Hz 0.4883 Hz
0.3516 Hz 0.3516 Hz 0.4492 Hz
0.4688 Hz 0.4883 Hz 0.6641 Hz
0.4492 Hz 0.4883 Hz 0.5273 Hz
6.4.3 When the thickness of brick wall is 0.22 m
We have processed the data which is collected by varying distance between antennas
and wall and distance between wall and human target. As per Table 6.2, we have
varied distance between antenna and wall from 0.5m to 1.5 m in step of 0.5m. These
various steps are taken as case 4 to case 6 for analysis and are described below. In case
4, the distance between wall and human target is varied and data is collected.
6.4.3.1 Detection of position of different peaks with amplitude
Case 4: When distance between Antenna and Brick-wall is 0.5m
The data as described in case number 4 shown in Table 6.2 is taken for analysis. The
measurements were taken when the distance between antennas and brick wall is at a
fixed distance of 0.5m and the distance of target from the wall is varied as 0.5 m, 1 m,
and 2 m. Figure 6.14 show the normalized result obtained when the distance between
target and wall is 0.5 m. From Fig. 6.14, the first peak is due to reflection from
antennas coupling, second peak is due to reflection from front side of brick wall, third
peak is due to reflection from back side of brick wall and the fourth is due to human
target.
When the target is at distance of 0.5 m from wall the actual distance from antenna to
target is 1.22m and observed distance from Fig. 6.14 is 1.725m which is also shown in
Table 6.8. In Fig. 6.14, the first peak due to reflection from antenna coupling is at
position 0.45m whose amplitude is of 0.2749. Second peak is due to reflection from
96 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
front side of brick wall is at position 0.75m whose amplitude is 1. The third peak is due
to back side of brick wall is observed at position 1.425m whose amplitude is 0.1149.
Next peak is due to the target position at 1.75m whose amplitude is 0.1834.
Table 6.8 shows location and amplitude of various peaks, in which sr. no. 1 shows the
result when target is at the 0.5m from brick wall, sr. no. 2 shows the result when target
is at 1m from brick wall, sr. no. 3 is when target is at 2m from brick wall. It is observed
from the results obtained in Table 6.8 that as distance between antennas and target
increases, the amplitude at target reduces. Also there are other peaks apart from peaks
due to objects present in scene of measurements. The reasons for the presence of this
peak may be due to multipath.
Fig. 6.14 For case 4: Distance between wall and human target is 0.5m
Table 6.8 Position and amplitude when distance between wall and target is 0.5m
Sr.
No.
Distance
between
wall and
human
target
First peak due
to antenna
Second peak
due To front
side of brick
wall
Third peak due
to back side of
brick wall
Fourth peak
due to target
Position Amp. Position Amp. Position Amp. Position Amp.
6.4 Result and Discussion 97
Case 5: When distance between Antenna and Brick-wall is 1m
Measurements are taken when the antenna is at a fixed distance of 1m from wall and
the distance (position) of target is changed from the wall as 0.5m, 1m, and 1.5m. When
the target is at distance of 0.5 m from wall the actual measurement from antenna to
target is 1.72 m and observed measurement from Fig. 6.15 is 2.175m and also shown in
Table 6.9. In Fig. 6.15, the first peak due to reflection from antenna is at position 0.45m
whose amplitude is of 0.2815. Second significant peak due to reflection from front side
of brick wall is at position 1.275 m whose amplitude is 1. Third peak is due to back side
of brick wall is at position 1.725m whose amplitude is 0.1167. Fourth peak is due to the
target is at position 2.175m whose amplitude is. Table 6.9 shows position and
amplitude of various peak, in which sr. no. 1 shows the result when target is at the 0.5m
from brick wall, sr. no. 2 shows the result when target is at 1m from brick wall and sr.
no. 3 is when target is at 1.5m from brick wall.
Fig. 6.15 For case 5: Distance between wall and human target is 0.5m
1. 0.5m 0.45m 0.2749 0.75m 1 1.425m 0.1149 1.725 m 0.1834
2. 1 m 0.45m 0.2735 0.75m 1 1.425m 0.1147 2.175 m 0.1002
3. 2 0.45m 0.273 0.75m 1 1.425m 0.1138 3.3 m 0.03445
98 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
Table 6.9 Position and amplitude when distance between wall and target is 1m
Case 6. When distance between Antenna and Brick-wall is 1.5m
Measurements are taken when antennas are at a fixed distance of 1.5 m from brick wall
and the position of target from the wall are changed from 0.5 m to 1.5 m in step of 0.5
m. The results obtained after processing are tabulated in Table 6.10. In below Fig. 6.16,
the data tip at 2.7m indicates reflection due to human target. The positions and
amplitudes at different objects present in scene are given in Table 6.10.
Table 6.10 shows position and amplitude of various peaks, in which sr. no. 1 shows the
result when target is at the 0.5m from brick wall, sr. no. 2 shows the result when target
is at 1m from brick wall, and sr. no. 3 is when target is at 1.5m from brick wall. It is
observed from results shown in Table 6.10, that as the distance between antennas and
target increases, detection of target peak becomes difficult due to very low amplitude
value.
Sr.
No.
Distance
between
wall and
human
target
First peak due
to antenna
Second peak
due To front
side of brick
wall
Third peak due
to back side of
brick wall
Fourth peak
due to target
Position Amp. Position Amp. Position Amp. Position Amp.
1. 0.5m 0.45m 0.2815 1.275 1 1.725 0.1167 2.175 0.1076
2. 1 m 0.45m 0.2818 1.275 1 1.725 0.117 2.85 0.03321
3. 1.5m 0.45m 0.2817 1.275 1 1.725 0.1165 3.3 0.03425
6.4 Result and Discussion 99
Fig. 6.16 For case 6: Distance between wall and human target is 0.5m
Table 6.10 Position and amplitude when distance between wall and target is 1.5m
6.4.3.2 Detection of location of Human being
From flowchart given in Fig.6.2, after observing range profile the distance to target is
noted down as shown in Table 6.5. It is observed from the results that there is
difference between the actual distance between antennas and target and measured
distance at target. The difference is given in last column of Table 6.5. The reasons for
difference are due to change in velocity of wave when it propagates through different
medium other than air.
Sr.
No.
Distance
between
wall and
human
target
First peak due
to antenna
Second peak
due To front
side of brick
wall
Third peak due
to back side of
brick wall
Fourth peak
due to target
Position Amp. Position Amp. Position Amp. Position Amp.
1. 0.5m 0.45m 0.4481 1.725 1 2.325 0.1085 2.7 0.0121
2. 1 m 0.45m 0.4473 1.725 1 2.325 0.1086 3.3 0.0616
3. 1.5m 0.45m 0.4478 1.725 1 2.325 0.1087 4.275 0.0261
100 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
6.4.3.3 Location of Human being after Velocity correction
For example, from Table 6.11, from case 4, if first row is considered, the distance
between antenna and wall is 0.5m and distance between wall and target is 0.5 m. The
total distance between antennas and target by considering wall thickness as 0.22 m will
be 1.22 m. But the observed result is 1.75 m. Due to external calibration there is
correction of 0.2 m which gives the distance as 1.55m instead of 1.75m. Hence the
difference of 1.55-1.22=0.33 m shows the deviation of actual location because of
decrease in velocity of microwave when the wave propagates through brick wall. This
effect can be minimized by using velocity correction. The corrected location of human
target related to antenna can be calculated by equation (6.1). For the remaining data
using eq. 6.1, the actual location is compared with corrected location and errors are
calculated which are shown in Table 6.11. The distance after correction almost matches
with the actual measurement, with error around 0.07m which falls in acceptable range
as range resolution is 0.075 m. Thus the values of error are acceptable. The Table 6.11
also gives the maximum values of standard deviation (SD) on the basis of which the
location of human target is obtained.
Table 6.11 Location obtained from Experimental data
Sr
no.
Brick
wall
thickn
ess
(m)
Distanc
e
betwee
n
Antenn
a and
wall
(m)
Distan
ce
betwe
en
wall
and
target
(m)
Measur
ed
Total
distanc
e (m)
Distance
to target
observed
from result
(m)
True
Distance
after
correction
external and
velocity (m)
(0.2959+0.2)
=0.4959
Differen
ce
Between
Measure
d and
observe
d
distance
Maximu
m SD
Value
observe
d at
human
target
location
Cas
e
4
0.22 0.5 0.5 1.22 1.725 1.229 0.0091 0.0101
0.22 0.5 1 1.72 2.175 1.6791 0.0409 0.0156
0.22 0.5 2 2.72 3.3 2.8041 0.0841 0.0039
Cas
e
5
0.22 1.0 0.5 1.72 2.25 1.7541 0.0341 0.0139
0.22 1.0 1.0 2.22 2.85 2.3541 0.1341 0.0118
0.22 1.0 1.5 2.72 3.3 2.8041 0.0841 0.0068
Ca
se
6 0.22 1.5 0.5 2.22 2.7 2.2041 0.1341 0.0159
0.22 1.5 1.0 2.72 3.3 2.8041 0.0841 0.0075
6.4 Result and Discussion 101
0.22 1.5 2.0 3.72 4.275 3.816 0.096 0.0035
6.4.3.4 Extraction of Amplitude variation from target location
For better understanding, the amplitude values for detected peak i.e., peak due to
antenna coupling, due to front side of wall, due to back side of wall and due to human
target for all 1024 traces were recorded. It is clearly observed that for the first three
peak i.e., peak due to antenna coupling, due to front side of wall, and due to back side
of wall there is no variation in amplitude for all 1024 traces. There is significant
amplitude variation at human target location. So in the next step only amplitude
variation portion is extracted.
Instead of extracting amplitude from single location, succeeding and preceding location
i.e., total three location amplitude value is taken and there average is calculated for all
1024 traces. This average value at all 1024 traces is taken and a plotted. The time
domain plots are obtained for all the cases i.e., case 4, case 5 and case 6 data sets. The
average amplitude variation plot at target location is shown in Fig. 6.17 for different
distances between wall and target for case 4 data set. Similarly for case 5 and case 6
data set, the average amplitude variations at target location is plotted in Fig. 6.18 and
Fig. 6.19 respectively.
a.when target is at 0.5m from wall b. when target is at 1m from wall
102 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
c. when target is at 2m from wall
Fig. 6.17 Breathing frequency extraction using FFT for case 4 dataset
(a) when target is at 0.5m from wall (b) when target is at 1m from wall
(c ) when target is at 1.5m
6.4 Result and Discussion 103
Fig. 6.18 Amplitude extraction using SD for case 5 dataset
(a) when target is at 0.5m from wall (b) when target is at 1m from wall
(c ) when target is at 2m
Fig. 6.19 Amplitude extraction using SD for case 6 dataset
6.4.3.5 Detect breathing frequency using FFT
After applying the FFT to the amplitude variations, the results are obtained and are
rightly shown in Fig. 6.20 for case 4 dataset, Fig. 6.21 for case 5 dataset and Fig. 6.22
for case 6 data set. In frequency spectrum, the peak is detected and corresponding
breathing frequency in the range of 0.2-0.6 Hz for human are noted as shown in Table
6.12. In this experimental study heartbeat frequency (1.2-1.7 Hz) was not detected due
to its relatively weak amplitude variation. It is observed from results, that there are
multiple frequencies present which are due to harmonics and clutter.
104 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
a.when target is at 0.5m from wall b. when target is at 1m from wall
(c ) when target is at 2m
Fig. 6.20 Breathing frequency extraction using FFT for case 4 dataset
(a) when target is at 0.5m from wall (b) when target is at 1m from wall
6.4 Result and Discussion 105
(c ) when target is at 1.5m
Fig. 6.21 Breathing frequency extraction using FFT for case 5 dataset
a. when target is at 0.5m from wall (b) when target is at 1m from wall
(c ) when target is at 2m
106 Effect of Thickness of wall on detection of location and breathing frequency
of human being standing behind the brick wall
Fig. 6.22 Breathing frequency extraction using FFT for case 6 dataset
Table 6.12 Breathing frequency values for different datasets
Case 4 Case 5 Case 6
0.4102 0.5078 0.293
0.3125 0.4297 0.293
0.5469 0.4688 0.2734
6.5 Conclusion
This chapter demonstrates the extraction of human breathing frequency when the
human is standing behind brick wall of thickness 32 cm and 22 cm. Data were collected
by changing distance from antenna to wall from 0.5m to 2m. As well as distance
between wall and target was varied from 0.5m to 2m. It is observed from results, as the
distance between antennas and wall increases, detection of target peak becomes
difficult due to very low amplitude value. The reason may be due to the swath of
antenna, which indicates the reduction in power as distance increases. From this it is
concluded that the antenna beam width plays important role in deciding the detection
capability of the target.
From the range profiles, it is observed that there are other peaks apart from peaks due to
objects present in scene of measurements. The reasons for the presence of this peak
may be due to multipath. Also it is observed from the comparison of results obtained
from case 1 and case 2, that the number of reflections increases due to multi paths when
the distance between antenna and target increases.
The location of human target is obtained using standard deviation which gives slightly
different location when compared with actual distance kept during experiments. To
correct the location external calibration and velocity correction is carried out. In spite of
correction there is difference in actual and obtained location. The possible reasons
could be human error while standing as a human target and range resolution error.
6.5 Conclusion 107
The amplitude variation plots described in Section 6.4.2.3 and 6.4.3.4 shows that there
are multiple frequencies present in the signal i.e., it includes breathing signal, heartbeat
signal and some clutter i.e., plots are not sinusoidal. Thus it makes extraction of
breathing frequency more complex. Advanced signal processing technique can be
developed to improve the performance of extraction of heartbeat signal.
The experimental results from all observations show that FFT method has successfully
extracted the breathing frequency of human target. Apart from breathing frequency,
there are many other frequencies or harmonics present in the output whose source is
unknown.
7.1 Summary of contributions 109
Chapter 7 Conclusion
The main objective of this research project was to explore different ways to enhance the
performance of a human breathing detection system. To address areas in which
improvement is required is to increase the signal strength of human target by reducing
clutter due to stationary objects present in the scene of observations, automatic
detection of location of human target and extraction of life sign of human target with
reduced errors. There are number of different techniques which researchers have used
to solve these problems. These techniques were reviewed in Chapter 2.
7.1 Summary of contributions
Signal processing methods are used by human breathing detection radar system for
extraction of breathing frequency. Successful results are obtained using FFT and EMD-
HHT methods. Also due to increase in distance between antenna and human being, the
value of respiration frequency does not changed. The experimental results also show
that FFT method is useful for extraction of breathing frequency of human being after
using band pass filter. It is observed that when the distance between antenna and target
is increased, the amplitude of breathing signal reduces. Successful extraction of
breathing frequency is achieved when human target is standing behind wall of different
types and of different thickness i.e., 0.2 cm thick plywood, brick wall of thickness 10
cm, 22 cm and 32 cm. It is also observed that, as the human target is same for data
collected when plywood wall and brick wall is used, the respiration frequency values
remains same. It is also observed from results that as the distance between antennas
and wall increases, detection of target peak becomes difficult due to very low amplitude
value.
The proposed methods not only extract the breathing frequency, but also estimate the
location of human being using standard deviation (SD). The determination of higher SD
value helps in the automatic detection of presence of life sign signal of human being
along with location. The location of human target is obtained using SD which gives
slightly different location when compared with actual distance kept during experiments.
To correct the location external calibration and velocity correction is carried out. In
spite of correction there is difference in actual and obtained location. The possible
reasons could be human error while standing as a human target and range resolution
error.
Increase in target signal strength is useful for process of extraction of life sign signal.
The focus is to explore the possibility of improvement in the detection and extraction of
breathing frequency of human being positioned behind the brick wall. After applying
clutter reduction technique using SVD, clutters are successfully minimized which
implies that the technique is powerful. Target signal strength is improved further by
using all Singular components related to target. Due to use of clutter reduction target
signal strength increases which increases the probability of correct target detection.
7.2 Below is a list of the major contributions in this
research:
1. Reduced the clutter in human breathing detection which increased the dynamic range
and hence the distance between radar and human target.
2. Human target location is determined using SD which is used for automatic
determination of position of human target behind wall.
3. Breathing frequency of human target is extracted successfully in all the
measurements.
7.3 Suggestions for Future Work 111
7.3 Suggestions for Future Work
The detection of human target hidden behind wall was studied. The proposed human
detection algorithm reduced clutter to enhance target signal strength and extract
breathing frequency.
There are several different ways this research could be extended in the future. Some of
these are as follows:
Chapter 3 demonstrates the extraction of human breathing using two different signal
processing techniques i.e., FFT and HHT. Since EMD-HHT technique was used when
plywood was used as a wall, in future, need to apply EMD-HHT algorithm by using
brick wall. The another problem with HHT method is to determine which IMF should
be selected as many IMFs have a frequency spectrum in the range of the human
breathing frequency range. In future, advanced signal processing techniques will be
explored to extract heart beat frequency which is not detected.
In chapter 4, monostatic radar system is used to measure transmission parameter S21
with the help of circulator. The experimental results are improved compared to
measurement of reflection parameter S11 when the distance between radar and target is
increased. Since the wide bandwidth circulators are not available in the required
frequency range, desired results could not be achieved.
The effect of non stationary clutter i.e., other movements in nearby region of the
detection is not considered in this work. In future study, brick wall can be replaced by
different complex type of walls like concrete with metal inside can be considered and
its effect on the detection can be studied.
Real time analysis for the detection and extraction of life sign frequencies is one of the
major interests of users. The developed techniques may be explored to be applied for
real time analysis.
From the range profiles, it is observed that there are other peaks apart from peaks due to
objects present in scene of measurements. The reasons for the presence of this peak
may be due to multipath. Also it is observed from the comparison of results obtained
that the number of reflections increases due to multi paths when the distance between
antenna and target increases.
The experimental results from all observations show that FFT method has successfully
extracted the breathing frequency of human target. Apart from breathing frequency,
there are many other frequencies or harmonics present in the output whose source is
unknown. This present research could be extended by studying reasons behind the
presence of various frequencies.
The amplitude variation plots described in Section 6.4.2.3 and 6.4.3.4 are not
sinusoidal. Thus it makes extraction of breathing frequency more complex. Signal
processing technique can be developed to improve the performance of extraction of
breathing.
Although the proposed method for human breathing detection can detect single target, a
goal for the future will be to detect multiple targets behind a wall. The main problem in
multi-target detection is the existence of false targets generated by multipath for the
reflected waves.
We have successfully extracted breathing frequency, but heartbeat signal is not
detected. Advanced signal processing technique can be developed to extract heartbeat
signal. This research could be extended by studying the human breathing detection
under rubble.
113
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Appendix A :Authors Contributions
A.1 Conference Publications
The details of paper presented in conferences are given below.
1. Improvement in detection of human life sign hidden behind the wall using
clutter reduction technique, IEEE International Conference on Emerging trends
in communication technologies, 18-19 Nov. 2016, Graphic Era University,
Dehradun, Uttarakhand India.
2. "Experimental study and analysis of stepped-frequency continuous wave based
radar for through the wall detection of life signs," 2016 IEEE Region 10
Conference (TENCON), Singapore, 22-25 Nov., 2016, pp. 1565-1569.
3. Extraction of Life Sign of Human Being Hidden behind the Wall, 11 th
International Conference on Industrial &Information systems, 03-04 Dec. 2016,
RailTel, IITR centre of Excellence in Telecom. Department of Electronics and
Communication, IIT Roorkee.
A.2 Journal Contributions
1. “Extraction of breathing frequency of human being hidden behind the wall
using different signal processing techniques,” JETIR,Vol. 5, (7), pp.223230,
July 2018.
2. “Improved Detection and Extraction of Human Life Sign
Hidden Behind the Brick Wall Using Stepped-Frequency
A-2 :Authors Contributions
Continuous Wave Radar,” JETIR, Volume 5, Issue 9, pp.471480, September
2018.
Appendix B :Photographs
Photographs taken during experimentations of equipments, components, constructed
brick wall, during measurements.
Handheld Vector Network Analyzer with components
B-2 :Photographs
Constructed Brick walls with thickness 0.22m and 0.32m
Measurements with 0.1 m thick brick wall
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