Research Article Human Respiration Localization Method Using...

Research ArticleHuman Respiration Localization Method Using UWB LinearAntenna Array

Yuan Liu12 Shiyou Wu1 Jie Chen1 Guangyou Fang1 and Hejun Yin3

1Key Laboratory of Electromagnetic Radiation and Sensing Technology Institute of Electronics Chinese Academy of SciencesBeijing 100190 China2University of Chinese Academy of Sciences Beijing 100049 China3Chinese Academy of Sciences Beijing 100864 China

Correspondence should be addressed to Jie Chen chenjiemailieaccn

Received 17 October 2014 Revised 21 January 2015 Accepted 21 January 2015

Academic Editor Chenzhong Li

Copyright copy 2015 Yuan Liu et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Human respiration is the basic vital sign in remotemonitoringThere has been remarkable progress in this area but some challengesstill remain to obtain the angle-of-arrival (AOA) and distinguish the individual signals This paper presents a 2D noncontacthuman respiration localization method using Ultra-Wideband (UWB) 1D linear antenna array The imaging reconstruction basedon beamforming is used to estimate the AOA of the human chest The distance-slow time 2D matrix at the estimated AOA isprocessed to obtain the distance and respiration frequency of the vital sign The proposed method can be used to isolate signalsfrom individual targets when more than one human object is located in the surveillance space The feasibility of the proposedmethod is demonstrated via the simulation and experiment results

1 Introduction

Impulse Ultra-Wideband (UWB) radar technology hasbecomemore andmore attractive to the researchers ofmicro-wave sensing and imaging It offers the advantages of highrange resolution and good penetration for nonmetallic mate-rials It has been implemented in various systems for dif-ferent applications such as ground penetrating radarthrough-wall imaging and concealed weapon detection [1]In recent years it has become a promising technique for non-contact detection of human respirationwhich is the basic vitalsign for health monitoring disaster search-and-rescue lawenforcement and urbanwarfare A typical human respirationcauses chest displacement from 01mm to several millimetersdepending on the target

The conventional measurement system consists of twoUWB antennas for transmission and reception respectively[2] When two or more targets are located in the field of viewit will be more challenging to isolate signals from individualtargets It generally extracts the respiration frequency andrange without the information of the angle-of-arrival (AOA)Another challenge is the interference caused by the random

body movement which results in low signal-to-noise ratio(SNR) in practical applications

Array-based systems have been used to improve theperformance of vital sign estimation Li and Lin [3] used 2transceivers one in front of and the other behind the humanbody to cancel out 1D random body movement Yu et al [4]placed 4 transceivers at 4 sides of the human body to cancelout 2D random body movement and improve detection sen-sitivity Akiyama et al [5] used the system with one transmit-ting antenna and 4 receiving antennas to improve the SNRwith correlation processing Takeuchi et al [6] utilized thevalue of correlation coefficient to measure the 3D locationwith 2D array antennas Su et al [7] determined AOA ofhuman target from the intersection of AOA information withtwo phased array Doppler radars

In this paper a new method was proposed to determineAOA and separate useful scattered signals from multipleindividuals with 1D linear antenna array Modified Kirchhoffimagingmethod based on beamformingwas used to combinethe information from the multiple antennas at given slow-time frame The AOA of human respiration was estimatedfrom the reconstructed images of all slow-time frames Then

Hindawi Publishing CorporationJournal of SensorsVolume 2015 Article ID 601926 11 pageshttpdxdoiorg1011552015601926

2 Journal of Sensors

we evaluated the respiration frequency and distance at theestimated AOA Even if individual respiration frequenciesand distances were very similar it would be possible to dis-tinguish different human targets based on AOA The perfor-mance of the proposed method was validated by simulationand experiment for SIMO system

The rest of this paper is organized as follows Section 2describes the vital signmodel for impulseUWB radar systemIn Section 3 a description of the algorithm implementationand the data processing steps are given Section 4 presents thenumerical simulations that have been carried out for testingthis algorithm Moreover experimental results are presentedin Section 5 Finally Section 6 concludes this paper

2 UWB Vital Sign Model

The vital sign is detected by observing the change of round-trip time of reflected signal fromhuman chestThe round-tripdelay of human chest in fast time is assumed to be a sinusoidalfunction

120591119903(119905) = 120591

0+ 120591119889sin (2120587119891

119903119905) (1)

where 1205910is the round-trip delay of the chest vibration center

120591119889is the time corresponding to the displacement amplitude

of respiration 119891119903is the respiration frequency and 119905 indicates

the slow time which corresponds to the acquisition time ofdistance profiles

For impulse UWB radar 119906(120591) is the transmitted impulsesignal and 120591 represents the fast time which correspondsto the distance dimension and is orthogonal to the slow-time dimension The received signal 119877(120591 119905) with respirationmotion of one human target can be expressed as [2 8]

119877 (120591 119905) = 119906 (120591 minus 120591119903(119905)) lowast ℎ

119903(120591)

+

119875

sum

119901=1119901 =119903

119906 (120591 minus 120591119901) lowast ℎ119901(120591) + 119908 (120591)

(2)

where lowast denotes convolution ℎ119903(120591) is the impulse response

of respirationmotion and ℎ119901(120591) is the joint impulse response

of transmitting antenna receiving antenna and 119901th staticobject 120591

119901is the round-trip delay of 119901th static object 119908(120591) is

the additive Gaussian white noiseThe captured signal is digitized and saved in a 2D data set

denoted by 119877[119898 119899]

119877 [119898 119899] = ℎ [119898 119899] + 119888 [119898 119899] + 119908 [119898 119899] (3)

where119898 = 0 1 119872 minus 1 represents the fast-time index and119899 = 0 1 119873minus1 introduces the slow-time indexThe signalis sampled at fast-time sampling interval of 120575

120591 The slow time

is discretized with 119905 = 119899119879119904 and 119879

119904is slow-time sampling

interval ℎ[119898 119899] denotes the response of respiration motion119888[119898 119899] denotes the static clutter due to the reflected signalsfrom static objects and the coupling between transmittingand receiving antennas 119908[119898 119899] denotes the additive Gaus-sian white noise

3-D raw data(MtimesNtimes L)

Signal preprocessing

Imaging reconstruction

Angle-of-arrivalestimation

Distance and frequencyestimation

Figure 1 Flowchart of respiration detection algorithm

For linear antenna array with 119871119879119909

transmitting antennasand 119871

119877119909receiving antennas the number of the equivalent

radar channels is equal to 119871 = 119871119879119909

times 119871119877119909 The data set will be

extended to a 3D matrix 119877[119898 119899 119897] where 119897 = 0 1 119871 minus 1

indicates that the data is captured by the 119897th radar channel

3 Detection Algorithm

In this section the algorithm is proposed to obtain vitalinformation by UWB linear arrayThe implement steps of thealgorithm are illustrated in the flowchart shown in Figure 1The algorithm consists of four main steps

31 Signal Preprocessing The implement steps of signal pre-processing for each radar channel are illustrated in the flow-chart shown in Figure 2 The preprocessing consists of threesteps

311 DC Offset Removal Since the sampling gate of theimpulse UWB receiver exhibits some DC offset DC offsetremoval is necessary to ensure that the mean value in fast-time dimension is near to zero Assume that the amplitudeprobability distribution in fast-time dimension is symmetricabout the mean value and the short time mean value isconstant over the time window A simple way to remove DCoffset is processed by subtracting themean value over the fast-time window

1198771015840

[119898 119899] = 119877 [119898 119899] minus

1

119872

119872minus1

sum

119898=0

119877 [119898 119899] (4)

312 Linear Trend Suppression Thedata acquisition typicallyaccompanies time drift caused by the instability of the trig-gering unit in impulse UWB radar It generally results in

Journal of Sensors 3

2-D raw data(MtimesN)

DC offset removal

Linear trendsuppression

SNR improvement

Figure 2 Flowchart of signal preprocessing

linear trend in slow time Linear trend suppression (LTS)method is used to remove the linear trend in 119877[119898 119899] [9]The potential linear trend in the slow-time dimension isestimated by means of a linear least-square fit The resultmatrix [119898 119899] is subsequently obtained by subtracting theestimated potential linear trend from 119877

1015840

[119898 119899]

R119879 = R1015840119879 minus x (x119879x)minus1

x119879R1015840119879 (5)

where x = [119899119873 1119873] 119899 = [0 1 119873minus1]

119879 1119873is119873times1 vector

containing unit values

313 SNR Improvement A moving average is applied alongthe fast-time and slow-time dimensions for SNR improve-ment It is commonly used to suppress the high-frequencyclutter interferences In addition the high-frequency noise isoften caused by the oversampling of backscattered signal Soan infinite impulse response (IIR) band-pass filtering on thedata set R in fast-time dimension is also performed as needed

32 Imaging Reconstruction Based on ldquotime delay-and-sumrdquobeamforming the conventional back-projection (BP) algo-rithm has been successfully implemented in UWB imagingreconstruction [10] In this method the output is formedby summing delayed versions of the received signals Thedelays are determined by the array geometry and the desiredscanning angle This approach is straightforward and easy toimplement However it has some undesirable characteristicssuch as high side-lobes because it is not based on the rigorouswave theory

An alternative to BP algorithm is Kirchhoff migra-tion [11] which is based on wave equation ConventionalKirchhoff migration is only feasible for monostatic array Itis not applicable to use conventional Kirchhoff migrationfor SIMOMIMO UWB array imaging Modified Kirchhoffmigration was developed for MIMO UWB array in [12] For

1-D SIMO linear array the modified Kirchhoff migration isformulated in Cartesian coordinate as

119868119905(r) = int (cos120593

1+ cos120593

2)

11988911198892

V120597

120597120591

119877(

1198891+1198892

V 119905 r1015840)119889119909

1015840

(6)

where I119905is the reconstructed image at slow-time 119905 119889

1and

1198892denote the propagation distance from migrated point

r(119909 119910) to the transmitting and receiving antennas respec-tively r1015840(1199091015840 1199101015840) represents the location of receiving antenna119877(120591 119905 r1015840) is the received signal at r1015840 for slow time 119905 120593

1is

the geometric angle between the distance direction and thetransmitting antenna 120593

2is the geometric angle between the

distance direction and the receiving antenna V is the velocityof electromagnetic wave in the medium

In this paper the polar coordinate is used to estimateAOA of human chest The pole is located at the center ofthe linear array The surveillance space is divided into pixelsby the distance and angle At given slow-time frame 119899 119871radar channels are used to reconstruct the image of targetsThe imaging intensity 119868

119899[120593119901 119903119902] at migrated point r(120593

119901 119903119902)

in polar coordinate can be calculated using the discreteformulation of (6)

119868119899[120593119901 119903119902] =

119871minus1

sum

119897=0

(cos120593119879119909119897

+ cos120593119877119909119897

)

sdot

119889119879119909119897

(120593119901 119903119902) 119889119877119909119897

(120593119901 119903119902)

VΔ119877 [119896 119899 119897]

119896 = floor(119889119879119909119897

(120593119901 119903119902) + 119889119877119909119897

(120593119901 119903119902)

V120575120591

)

(7)

where 120593119901(119901 = 0 1 119875 minus 1) denotes the polar angle

and 119903119902(119902 = 0 1 119876 minus 1) denotes the radial distance 119897

indicates the 119897th radar channel For the 119897th radar channel 120593119879119909119897

represents the geometric angle between the distance directionand the transmitting antenna 120593

119877119909119897denotes the geometric

angle between the distance direction and the receivingantenna 119889

119879119909119897(120593119901 119903119902) is the propagation distance from the

transmitting antenna to the imaging pixel 119889119877119909119897

(120593119901 119903119902) is the

propagation distance from the imaging pixel to the receivingantennaThe index 119896 locates the data at the correct time delayfloor(119909) means the largest integer not greater than 119909 Δ isfinite difference along fast-time dimensionA complete imageis reconstructed by calculating the image intensity of all thepixels

The formation process of the slice of 3Dmatrix 119878[119903119902 119899 120593119901]

(distance slow-time and angle) at given angle 120593119901is shown

in Figure 3 From the reconstructed image 119868119899[120593119901 119903119902] at given

slow time 119899 we can get the distance profile at given angle120593119901as

shown in Figure 3(a)The slice of S at given angle120593119901is created

from the distance profiles for all slow-time indices The vitalsign of the human object might be in the interesting imagingcells of the radial zone which is marked by the red dottedframe in Figure 3(a) The distance changes periodically dueto the respiratory motion This quasiperiodic variation along


Slow time(a) (b)

Dist

ance

TxRx5 Rx8 Rx9Rx6 Rx7Rx4Rx3Rx2Rx1

Figure 3 Formation of the slice of S at given angle

slow time in the slice of S at given 120593119901 as shown in Figure 3(b)

can be used to estimate the respiratory frequency and dis-tance

33 AOA Estimation

331 Static Clutter Removal Before AOA estimation thestatic clutter needs to be removed from the slices of S for allangles The signals reflected from human chest are very weakin comparison to the signals scattered from static objects andthe coupling from the transmitting antenna to the receivingantennas So it is essential to separate these weak signals fromthe static clutter

There are different algorithms to remove the static clutter[13ndash15] In the case of vital sign detection the respirationresponse arrives mostly at the same round-trip delay in eachmeasured signal and is misinterpreted as a static clutterby those algorithms So the respiration response is almostundetectable with those algorithms We suppress the staticclutter using adaptive background subtraction method [16]which is based on the exponential averaging The newestimate of static clutter y

119899is calculated from the previous

estimate y119899minus1

and the signal x119899

y119899= 120572119899y119899minus1

+ (1 minus 120572119899) x119899 (8)

where 120572119899is a vector of weighting coefficient with the size

of [119876 times 1] y119899and x

119899are 1D vectors with the size [119876 times 1]

x119899represents the distance profile in the matrix 119878[119903

119902 119899 120593119901]

at given slow-time 119899 and angle 120593119901 The vector of weighting

coefficient120572119899is time-variant Each element in120572

119899is adaptively

adjusted between 0 and 1 Figure 4 shows an example of theresult after removing the static clutter in Figure 3(b) As canbe seen from Figure 3(b) the respiration response is blurredwith the strong static clutter In Figure 4 the static clutter isreduced and the respiration response is clearly highlighted ascompared to Figure 3(b)

332 AOA Evaluation After removing the static clutter wecan obtain the AOA of the human target from the peak value

Slow time

Dist

ance

Figure 4 Result after removing the static clutter

along angle dimension in the matrix S Assume that there isonly one human object located in the surveillance space

The matrix 119864[119899 119901] with the size of 119873 times 119875 is obtainedfrom the maximum value along the distance dimension in119878[119903119902 119899 120593119901] at given slow-time 119899 and angle 120593

119901 From 119864[119899 119901]

the AOA can be roughly evaluated from the index of themaximum value at given slow time 119899 In order to reduce thenoise and avoid false evaluation 119864[119899 119901] is averaged alongslow-time dimension

119864 [119901] =

1

119873

119873minus1

sum

119899=0

119864 [119901 119899] (9)

The AOA of the human target is given from the index of themaximum value in the vector E

120579 = 1205790 + argmax119901

119864 [119901] times Δ120579 (10)

where Δ120579 is the interval of scanning angle and 1205790 is the min-imum scanning angle


11m

11

m

PML

PML

PML

PML

Human 155

m


(a)

11m

11

m

PML

PML

PML

PML

Human 1Human 2

55m 55

m


(b)

Figure 5 (a) FDTD model of first simulation (b) FDTD model of second simulation

When more than one human target is located in the sur-veillance space the AOAs of human targets are estimatedfrom the indices of the local maximum values of the vectorE

34 Distance and Frequency Estimation The slice of thematrix S at the estimated AOA is processed to obtain the dis-tance and respiration frequency using the method in [2]Fourier transform is firstly performed in slow-time dimen-sion which generates the distance-frequency 2D matrix Thevital sign is identified in this matrix by using the methodbased on constant false alarm ratio (CFAR) and clustering

4 Simulations and Results

41 Simulation Model and Parameters With 2D finite-dif-ference time-domain (FDTD) method the received signalsfrom the displacement of human chest were simulated for aSIMO systemwith one transmitting antenna and nine receiv-ing antennas The center antenna was used as both the trans-mitting and receiving antenna The chest model was built asa metallic cylinder with the radius of 02m The respirationmotion of the chest was simulated as the periodic variation ofthe radius The transmitted signal was the second derivativeof Gaussian pulse with the center frequency of 400MHz

Two simulations were conducted to prove the perfor-mance of the proposed method In the first simulationthe cylinder vibrated with the frequency of 05Hz It waslocated at 55m away from the center of antenna array at theangle of 60∘ in free space Another cylinder with the samevibrating frequency was added in the second simulation Itwas located at the same distance but at a different angle of

Table 1 Simulation parameters

Parameters Quantity Value119863width The width of the simulated space 11m119863height The height of the simulated space 11m119877 Distance of chest center 55m119891119903

Respiration frequency 05Hz1198910

Center frequency 400MHz119872 Fast-time sampling number 2048119873 Slow-time sampling number 512120575120591

Fast-time sampling interval 40 ps

135∘ Figure 5 shows the 2D FDTDmodel of two simulationsThe simulation parameters are detailed in Table 1

42 Simulation Results Figure 6 shows the distance-fre-quency matrices for 9 channels using the respiration detec-tion method [2] in the first simulation The distances andrespiration frequencies are extracted and shown in Figure 7The respiration frequencies are the same for all channels(see Figure 7(b)) while the distances are different (seeFigure 7(a))

The simulated data in first simulation is processed usingthe proposedmethod Figure 8(a) plots the normalized valuesof each row in 119864[119899 119901] along the scanning-angle dimensionFrom Figure 8(a) the AOA of the respiration is evaluated as60∘ by (10) The distance-slow time 2D matrix at the angle of60∘ is processed to estimate the distance and frequency Thedistance-frequency matrix is shown in Figure 8(b) It showsthat the distance is 55m and the respiration frequency is05Hz


CH1

0506

0

2

4

6

8

10

CH2

0506

0

2

4

6

8

10

CH3

0506

0

2

4

6

8

10

CH4

0506

0

2

4

6

8

10

Respiration frequency (Hz)

CH5

0506

0

2

4

6

8

10

CH6

0506

0

2

4

6

8

10

CH7

0506

0

2

4

6

8

10

CH8

0506 0506

0

2

4

6

8

10

CH90

2

4

6

8

10

Dist

ance

(m)

Figure 6 Distance-frequency matrices for all channels in the first simulation

1 2 3 4 5 6 7 8 9

5515253545556575859

6

Channel index

Dist

ance

(m)

(a)

1 2 3 4 5 6 7 8 9

0010203040506070809

1

Channel index

Resp

iratio

n fre

quen

cy (H

z)

(b)

Figure 7 (a) The estimated distances for all channels (b) The estimated respiration frequencies for all channels

40 60 80 100 120 1400

02

04

06

08

1

Nor

mal

ized

ampl

itude

Scanning angle (∘)

(a)

Respiration frequency (Hz)01 02 03 04 05 06

0

2

4

6

8

10

Dist

ance

(m)

(b)

Figure 8 (a) The functions of the scanning angle in the first simulation (b) The distance-frequency matrix at 60∘


40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)

Figure 9 (a) The functions of the scanning angle in the second simulation (b) The distance-frequency matrix at 60∘ (c) The distance-fre-quency matrix at 135∘

For the second simulation the functions of the scanningangle are plotted in Figure 9(a) Two peaks indicate that twotargets are located at different angles The correspondingAOAs are extracted as 60∘ and 135∘ The distance-frequencymatrices for 60∘ and 135∘ are shown in Figures 9(b) and 9(c)The distances and frequencies of two targets are estimated at55m and 05Hz Even though the individual frequencies anddistances are the same the two targets can be distinguishedby the AOAs with SIMO system

5 Experiment and Results

51 Experimental Scenario Themeasurement setup as shownin Figure 10 consists ofGeozondas 8-channel digital samplingconverter SD-10820 Geozondas pulse generator GZ1118GN-03 linear antenna array and PC SD-10820 is used to trigger

the pulse generator and sample the signal using sequentialsampling technique The channel bandwidth of SD-10820 is02ndash20GHz A Gaussian pulse with 300 ps duration and 50 Vamplitude is sent by the pulse generator to the transmittingantenna PC sets the parameters of the sampling converterand gets the digitized data via USB interface The linearantenna array made up of 9 cavity-backed bowtie dipolesis used with an interelement space of 195mm The elementat the center of the linear array is used for the transmittingantenna The other 8 elements are receiving antennas

The radar parameters are shown in Table 2 Duringdata acquisition SNR was improved by averaging every 20received signals The total sampling points 119872 in fast-timedimension were set to 1000 It took 48 s to acquire 119873 = 512

echoes for the measurementThe scenario of the measurement is shown in Figure 11

Table 3 shows the locations of three human targets


SD-10820 sampling converter

Pulse generator

PC

Tx

Rx8 Rx6Rx7 Rx5 Rx4 Rx3 Rx2 Rx1

Trigger

USB

Figure 10 Experimental measurement setup

125∘ 60

∘90∘

Figure 11 Experimental scenario for respiration detection of threehuman targets

Table 2 Parameters of the UWB radar system

Parameters ValueOperating mode ImpulseAntenna bandwidth 05ndash25 GHzAntenna gain at 15 GHz 85 dBiPulse repetition frequency 500KHzTime window 40 nsSampling points (119872) 1000ADC resolution 14 bit

Table 3 Target locations

Distance AngleTarget 1 25m 60∘

Target 2 33m 90∘

Target 3 16m 125∘

52 Experimental Results Figure 12 shows the distance-fre-quency matrices for each one of 8 channels using the res-piration detection method in [2] As can be seen in Figure 12the vital sign with 042 Hz respiration frequency is clearlyhighlighted which corresponds to target 2 The respirationfrequencies from target 1 and target 3 are blurred due tolow SNR From the amplitude and the distance of respirationresponses we can tell that target 1 is closer to the 1st channeltarget 2 is located right ahead of the antenna array and target3 is closer to the 8th channel But the exact AOAs cannot beevaluated from these matrices The beamforming is used tocoherently combine the signals from all 8 channels to extractthe AOAs

The functions of the scanning angle are plotted inFigure 13(a) Three groups of peak values obviously appearat the scanning angles 60∘ 90∘ and 125∘ respectivelyFigure 13(b) gives the averaged and normalized function ofthe scanning angle fromwhich three estimatedAOAs are 60∘90∘ and 125∘

The distance-slow time 2D matrices at the angles of 60∘90∘ and 125∘ are obtained and used for the estimation ofthe distances and respiration frequencies The distance-fre-quency matrix at the angle of 60∘ is shown in Figure 14(a)The distance and respiration frequency can be estimatedat 25m and 02Hz Similarly the distance and respirationfrequency can be estimated at 33m and 042Hz from thedistance-frequency matrix at the angle of 90∘ shown inFigure 14(b) For the distance-frequency matrix at the angleof 125∘ shown in Figure 14(c) two vital signs are shown withone false sign The false sign is identified and removed usingthe method in [2] The distance and respiration frequenciesare estimated at 16m and 029Hz Comparing Figure 14 withFigure 12 SNR of the distance-frequency matrix is improvedbecause the beamforming coherently combines the multiple-channel signals and obtains processing gain

6 Conclusion

A novel respiration detection method based on UWB linearantenna array is presented in this paper This method canbe used to obtain the vital information including the angle-of-arrival and distance and respiration frequency Whenmore than one target is in the field of view the proposedmethod can estimate the AOAs of individuals Then wecan distinguish the different sources of respiration motionisolate the desired signals and determine the distance andrespiration frequency of individuals

It is noted that the AOA estimation can performwell onlyon the assumption that no more than one human object islocated at a specific scanning angle Further efforts shouldbe made to overcome this limitation and detect vital signs incomplex environment

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Respiration frequency (Hz)02 04 06 08

1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)

Figure 12 The range-frequency matrices for 8 channels (a) The 1st channel (b) The 2nd channel (c) The 3rd channel (d) The 4th channel(e) The 5th channel (f) The 6th channel (g) The 7th channel (h) The 8th channel


10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)

Figure 13 (a) The functions of the scanning angle (b) The averaging and normalized function of the scanning angle


1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)

Figure 14 The distance-frequency matrices (a) At the angle of 60∘ (b) At the angle of 90∘ (c) At the angle of 125∘


Acknowledgment

Funding for this work was provided by the National HighTechnology Research and Development Program of China(863 Program) under Grant no 2012AA061403

References

[1] D J Daniels EM Detection of Concealed Targets John Wiley ampSons Hoboken NJ USA 2009

[2] Y Xu SWu C Chen J Chen andG Fang ldquoA novelmethod forautomatic detection of trapped victims by ultrawideband radarrdquoIEEE Transactions on Geoscience and Remote Sensing vol 50no 8 pp 3132ndash3142 2012

[3] C Li and J Lin ldquoComplex signal demodulation and randombody movement cancellation techniques for non-contact vitalsign detectionrdquo in Proceedings of the IEEE MTT-S InternationalMicrowave Symposium Digest (MTT rsquo08) pp 567ndash570 IEEEAtlanta Ga USA June 2008

[4] X Yu C Li and J Lin ldquoTwo-dimensional noncontact vital signdetection using Doppler radar array approachrdquo in Proceedingsof the IEEE MTT-S International Microwave Symposium (IMSrsquo11) Baltimore Md USA June 2011

[5] I Akiyama N Yoshizumi A Ohya Y Aoki and F MatsunoldquoSearch for survivors buried in rubble by rescue radarwith arrayantennasmdashextraction of respiratory fluctuationrdquo in Proceedingsof the IEEE International Workshop on Safety Security andRescue Robotics (SSRR rsquo07) pp 1ndash6 Rome Italy September2007

[6] T Takeuchi H Saito Y Aoki A Ohya F Matsuno andI Akiyama ldquoRescue radar system with array antennasrdquo inProceedings of the 34th Annual Conference of the IEEE IndustrialElectronics Society (IECON rsquo08) pp 1782ndash1787 Orlando FlaUSA November 2008

[7] Y-S Su C-C Chang J-J Guo and S-F Chang ldquo2-D wirelesshuman subjects positioning system based on respiration detec-tionsrdquo in Proceedings of the IEEE MTT-S International Micro-wave Symposium (IMS rsquo12) pp 1ndash3 IEEE Montreal CanadaJune 2012

[8] S Venkatesh C R Anderson N V Rivera and R M BuehrerldquoImplementation and analysis of respiration-rate estimationusing impulse-based UWBrdquo in Proceedings of the IEEE MilitaryCommunications Conference (MILCOM rsquo05) pp 3314ndash3320Atlantic City NJ USA October 2005

[9] A Nezirovic A G Yarovoy and L P Ligthart ldquoSignal pro-cessing for improved detection of trapped victims using UWBradarrdquo IEEE Transactions on Geoscience and Remote Sensingvol 48 no 4 pp 2005ndash2014 2010

[10] M D Desai and W K Jenkins ldquoConvolution backprojectionimage reconstruction for spotlight mode synthetic apertureradarrdquo IEEE Transactions on Image Processing vol 1 no 4 pp505ndash517 1992

[11] N Bleistein ldquoOn the imaging of reflectors in the earthrdquo Geo-physics vol 52 no 7 pp 931ndash942 1987

[12] X Zhuge TG Savelyev AG Yarovoy andL P Ligthart ldquoUWBarray-based radar imaging usingmodified kirehhoffmigrationrdquoin Proceedings of the IEEE International Conference on Ultra-Wideband (ICUWB rsquo08) pp 175ndash178 Hannover GermanySeptember 2008

[13] M Piccardi ldquoBackground subtraction techniques a reviewrdquo inProceedings of the IEEE International Conference on Systems

Man and Cybernetics (SMC rsquo04) pp 3099ndash3104 Hague TheNetherlands October 2004

[14] AMcivor V Zang and R Klette ldquoThe background subtractionproblem for video surveillance systemsrdquo in Proceedings ofInternational Workshop on Robot Vision (RobVis rsquo01) pp 176ndash183 Auckland New Zealand February 2001

[15] AG Yarovoy P vanGenderen andL P Ligthart ldquoGroundpen-etrating impulse radar for landmine detectionrdquo inProceedings ofthe 8th International Conference on Ground Penetrating Radarpp 856ndash860 Gold Coast Australia May 2000

[16] R Zetik S Crabbe J Krajnak P Peyerl J Sachs and RThoma ldquoDetection and localization of persons behind obstaclesusingM-sequence through-the-wall radarrdquo in Proceedings of theSensors and Command Control Communications and Intelli-gence (C3I) Technologies for Homeland Security and HomelandDefense V Orlando Fla USA April 2006

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



we evaluated the respiration frequency and distance at theestimated AOA Even if individual respiration frequenciesand distances were very similar it would be possible to dis-tinguish different human targets based on AOA The perfor-mance of the proposed method was validated by simulationand experiment for SIMO system

The rest of this paper is organized as follows Section 2describes the vital signmodel for impulseUWB radar systemIn Section 3 a description of the algorithm implementationand the data processing steps are given Section 4 presents thenumerical simulations that have been carried out for testingthis algorithm Moreover experimental results are presentedin Section 5 Finally Section 6 concludes this paper

2 UWB Vital Sign Model

The vital sign is detected by observing the change of round-trip time of reflected signal fromhuman chestThe round-tripdelay of human chest in fast time is assumed to be a sinusoidalfunction

120591119903(119905) = 120591

0+ 120591119889sin (2120587119891

119903119905) (1)

where 1205910is the round-trip delay of the chest vibration center

120591119889is the time corresponding to the displacement amplitude

of respiration 119891119903is the respiration frequency and 119905 indicates

the slow time which corresponds to the acquisition time ofdistance profiles

For impulse UWB radar 119906(120591) is the transmitted impulsesignal and 120591 represents the fast time which correspondsto the distance dimension and is orthogonal to the slow-time dimension The received signal 119877(120591 119905) with respirationmotion of one human target can be expressed as [2 8]

119877 (120591 119905) = 119906 (120591 minus 120591119903(119905)) lowast ℎ

119903(120591)

+

119875

sum

119901=1119901 =119903

119906 (120591 minus 120591119901) lowast ℎ119901(120591) + 119908 (120591)

(2)

where lowast denotes convolution ℎ119903(120591) is the impulse response

of respirationmotion and ℎ119901(120591) is the joint impulse response

of transmitting antenna receiving antenna and 119901th staticobject 120591

119901is the round-trip delay of 119901th static object 119908(120591) is

the additive Gaussian white noiseThe captured signal is digitized and saved in a 2D data set

denoted by 119877[119898 119899]

119877 [119898 119899] = ℎ [119898 119899] + 119888 [119898 119899] + 119908 [119898 119899] (3)

where119898 = 0 1 119872 minus 1 represents the fast-time index and119899 = 0 1 119873minus1 introduces the slow-time indexThe signalis sampled at fast-time sampling interval of 120575

120591 The slow time

is discretized with 119905 = 119899119879119904 and 119879

119904is slow-time sampling

interval ℎ[119898 119899] denotes the response of respiration motion119888[119898 119899] denotes the static clutter due to the reflected signalsfrom static objects and the coupling between transmittingand receiving antennas 119908[119898 119899] denotes the additive Gaus-sian white noise

3-D raw data(MtimesNtimes L)

Signal preprocessing

Imaging reconstruction

Angle-of-arrivalestimation

Distance and frequencyestimation

Figure 1 Flowchart of respiration detection algorithm

For linear antenna array with 119871119879119909

transmitting antennasand 119871

119877119909receiving antennas the number of the equivalent

radar channels is equal to 119871 = 119871119879119909

times 119871119877119909 The data set will be

extended to a 3D matrix 119877[119898 119899 119897] where 119897 = 0 1 119871 minus 1

indicates that the data is captured by the 119897th radar channel

3 Detection Algorithm

In this section the algorithm is proposed to obtain vitalinformation by UWB linear arrayThe implement steps of thealgorithm are illustrated in the flowchart shown in Figure 1The algorithm consists of four main steps

31 Signal Preprocessing The implement steps of signal pre-processing for each radar channel are illustrated in the flow-chart shown in Figure 2 The preprocessing consists of threesteps

311 DC Offset Removal Since the sampling gate of theimpulse UWB receiver exhibits some DC offset DC offsetremoval is necessary to ensure that the mean value in fast-time dimension is near to zero Assume that the amplitudeprobability distribution in fast-time dimension is symmetricabout the mean value and the short time mean value isconstant over the time window A simple way to remove DCoffset is processed by subtracting themean value over the fast-time window

1198771015840

[119898 119899] = 119877 [119898 119899] minus

1

119872

119872minus1

sum

119898=0

119877 [119898 119899] (4)

312 Linear Trend Suppression Thedata acquisition typicallyaccompanies time drift caused by the instability of the trig-gering unit in impulse UWB radar It generally results in



DC offset removal


SNR improvement



1015840

[119898 119899]


x119879R1015840119879 (5)

where x = [119899119873 1119873] 119899 = [0 1 119873minus1]

119879 1119873is119873times1 vector






119868119905(r) = int (cos120593

1+ cos120593

2)

11988911198892

V120597

120597120591

119877(

1198891+1198892

V 119905 r1015840)119889119909

1015840

(6)


1and



1is






119901 119903119902)


119868119899[120593119901 119903119902] =

119871minus1

sum

119897=0

(cos120593119879119909119897

+ cos120593119877119909119897

)

sdot

119889119879119909119897

(120593119901 119903119902) 119889119877119909119897

(120593119901 119903119902)

VΔ119877 [119896 119899 119897]

119896 = floor(119889119879119909119897

(120593119901 119903119902) + 119889119877119909119897

(120593119901 119903119902)

V120575120591

)

(7)









(120593119901 119903119902) is the









Slow time(a) (b)

Dist

ance





33 AOA Estimation






y119899= 120572119899y119899minus1

+ (1 minus 120572119899) x119899 (8)


of [119876 times 1] y119899and x



119902 119899 120593119901]



119899is adaptively



Slow time

Dist

ance




119901 From 119864[119899 119901]


119864 [119901] =

1

119873

119873minus1

sum

119899=0

119864 [119901 119899] (9)


120579 = 1205790 + argmax119901

119864 [119901] times Δ120579 (10)



11m

11

m

PML

PML

PML

PML

Human 155

m


(a)

11m

11

m

PML

PML

PML

PML

Human 1Human 2

55m 55

m


(b)
















CH1

0506

0

2

4

6

8

10

CH2

0506

0

2

4

6

8

10

CH3

0506

0

2

4

6

8

10

CH4

0506

0

2

4

6

8

10


CH5

0506

0

2

4

6

8

10

CH6

0506

0

2

4

6

8

10

CH7

0506

0

2

4

6

8

10

CH8

0506 0506

0

2

4

6

8

10

CH90

2

4

6

8

10

Dist

ance

(m)


1 2 3 4 5 6 7 8 9

5515253545556575859

6

Channel index

Dist

ance

(m)

(a)

1 2 3 4 5 6 7 8 9

0010203040506070809

1

Channel index

Resp

iratio

n fre

quen

cy (H

z)

(b)


40 60 80 100 120 1400

02

04

06

08

1

Nor

mal

ized

ampl

itude


(a)


0

2

4

6

8

10

Dist

ance

(m)

(b)



40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











DC offset removal


SNR improvement



1015840

[119898 119899]


x119879R1015840119879 (5)

where x = [119899119873 1119873] 119899 = [0 1 119873minus1]

119879 1119873is119873times1 vector






119868119905(r) = int (cos120593

1+ cos120593

2)

11988911198892

V120597

120597120591

119877(

1198891+1198892

V 119905 r1015840)119889119909

1015840

(6)


1and



1is






119901 119903119902)


119868119899[120593119901 119903119902] =

119871minus1

sum

119897=0

(cos120593119879119909119897

+ cos120593119877119909119897

)

sdot

119889119879119909119897

(120593119901 119903119902) 119889119877119909119897

(120593119901 119903119902)

VΔ119877 [119896 119899 119897]

119896 = floor(119889119879119909119897

(120593119901 119903119902) + 119889119877119909119897

(120593119901 119903119902)

V120575120591

)

(7)









(120593119901 119903119902) is the









Slow time(a) (b)

Dist

ance





33 AOA Estimation






y119899= 120572119899y119899minus1

+ (1 minus 120572119899) x119899 (8)


of [119876 times 1] y119899and x



119902 119899 120593119901]



119899is adaptively



Slow time

Dist

ance




119901 From 119864[119899 119901]


119864 [119901] =

1

119873

119873minus1

sum

119899=0

119864 [119901 119899] (9)


120579 = 1205790 + argmax119901

119864 [119901] times Δ120579 (10)



11m

11

m

PML

PML

PML

PML

Human 155

m


(a)

11m

11

m

PML

PML

PML

PML

Human 1Human 2

55m 55

m


(b)
















CH1

0506

0

2

4

6

8

10

CH2

0506

0

2

4

6

8

10

CH3

0506

0

2

4

6

8

10

CH4

0506

0

2

4

6

8

10


CH5

0506

0

2

4

6

8

10

CH6

0506

0

2

4

6

8

10

CH7

0506

0

2

4

6

8

10

CH8

0506 0506

0

2

4

6

8

10

CH90

2

4

6

8

10

Dist

ance

(m)


1 2 3 4 5 6 7 8 9

5515253545556575859

6

Channel index

Dist

ance

(m)

(a)

1 2 3 4 5 6 7 8 9

0010203040506070809

1

Channel index

Resp

iratio

n fre

quen

cy (H

z)

(b)


40 60 80 100 120 1400

02

04

06

08

1

Nor

mal

ized

ampl

itude


(a)


0

2

4

6

8

10

Dist

ance

(m)

(b)



40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Slow time(a) (b)

Dist

ance





33 AOA Estimation






y119899= 120572119899y119899minus1

+ (1 minus 120572119899) x119899 (8)


of [119876 times 1] y119899and x



119902 119899 120593119901]



119899is adaptively



Slow time

Dist

ance




119901 From 119864[119899 119901]


119864 [119901] =

1

119873

119873minus1

sum

119899=0

119864 [119901 119899] (9)


120579 = 1205790 + argmax119901

119864 [119901] times Δ120579 (10)



11m

11

m

PML

PML

PML

PML

Human 155

m


(a)

11m

11

m

PML

PML

PML

PML

Human 1Human 2

55m 55

m


(b)
















CH1

0506

0

2

4

6

8

10

CH2

0506

0

2

4

6

8

10

CH3

0506

0

2

4

6

8

10

CH4

0506

0

2

4

6

8

10


CH5

0506

0

2

4

6

8

10

CH6

0506

0

2

4

6

8

10

CH7

0506

0

2

4

6

8

10

CH8

0506 0506

0

2

4

6

8

10

CH90

2

4

6

8

10

Dist

ance

(m)


1 2 3 4 5 6 7 8 9

5515253545556575859

6

Channel index

Dist

ance

(m)

(a)

1 2 3 4 5 6 7 8 9

0010203040506070809

1

Channel index

Resp

iratio

n fre

quen

cy (H

z)

(b)


40 60 80 100 120 1400

02

04

06

08

1

Nor

mal

ized

ampl

itude


(a)


0

2

4

6

8

10

Dist

ance

(m)

(b)



40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










11m

11

m

PML

PML

PML

PML

Human 155

m


(a)

11m

11

m

PML

PML

PML

PML

Human 1Human 2

55m 55

m


(b)
















CH1

0506

0

2

4

6

8

10

CH2

0506

0

2

4

6

8

10

CH3

0506

0

2

4

6

8

10

CH4

0506

0

2

4

6

8

10


CH5

0506

0

2

4

6

8

10

CH6

0506

0

2

4

6

8

10

CH7

0506

0

2

4

6

8

10

CH8

0506 0506

0

2

4

6

8

10

CH90

2

4

6

8

10

Dist

ance

(m)


1 2 3 4 5 6 7 8 9

5515253545556575859

6

Channel index

Dist

ance

(m)

(a)

1 2 3 4 5 6 7 8 9

0010203040506070809

1

Channel index

Resp

iratio

n fre

quen

cy (H

z)

(b)


40 60 80 100 120 1400

02

04

06

08

1

Nor

mal

ized

ampl

itude


(a)


0

2

4

6

8

10

Dist

ance

(m)

(b)



40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










CH1

0506

0

2

4

6

8

10

CH2

0506

0

2

4

6

8

10

CH3

0506

0

2

4

6

8

10

CH4

0506

0

2

4

6

8

10


CH5

0506

0

2

4

6

8

10

CH6

0506

0

2

4

6

8

10

CH7

0506

0

2

4

6

8

10

CH8

0506 0506

0

2

4

6

8

10

CH90

2

4

6

8

10

Dist

ance

(m)


1 2 3 4 5 6 7 8 9

5515253545556575859

6

Channel index

Dist

ance

(m)

(a)

1 2 3 4 5 6 7 8 9

0010203040506070809

1

Channel index

Resp

iratio

n fre

quen

cy (H

z)

(b)


40 60 80 100 120 1400

02

04

06

08

1

Nor

mal

ized

ampl

itude


(a)


0

2

4

6

8

10

Dist

ance

(m)

(b)



40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










40 60 80 100 120 1400

02

04

06

08

1N

orm

aliz

ed am

plitu

de


(a)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(b)


0

1

2

3

4

5

6

7

Dist

ance

(m)

(c)











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











Pulse generator

PC

Tx


Trigger

USB


125∘ 60

∘90∘






Target 2 33m 90∘

Target 3 16m 125∘




6 Conclusion







1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











1

2

3

4

5

6

Dist

ance

(m)

(a)


1

2

3

4

5

6

Dist

ance

(m)

(b)


1

2

3

4

5

6

Dist

ance

(m)

(c)


1

2

3

4

5

6

Dist

ance

(m)

(d)


1

2

3

4

5

6

Dist

ance

(m)

(e)


1

2

3

4

5

6

Dist

ance

(m)

(f)


1

2

3

4

5

6

Dist

ance

(m)

(g)


1

2

3

4

5

6

Dist

ance

(m)

(h)



10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










10

8

6

4

2

040 60 80 100 120 140


Am

plitu

detimes10

4

60∘

90∘

125∘

(a)

40 60 80 100 120 140


1

08

06

04

02

0

Nor

mal

ized

ampl

itude

X 60Y 02574

X 125Y 1

X 90

Y 01155

(b)



1

0

2

3

4

5

Dist

ance

(m)

(a)

1

0

2

3

4

5

Dist

ance

(m)


(b)


1

0

2

3

4

5

Dist

ance

(m)

(c)



Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Acknowledgment


References




















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Research Article Human Respiration Localization Method Using...

Documents

Transcript of Research Article Human Respiration Localization Method Using...