[IEEE IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium - Honolulu, HI,...
Transcript of [IEEE IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium - Honolulu, HI,...
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ENVIRONMENTAL MONITORING WITH THE IMAGING MIMO RADARSMIRA-CLE AND MIRA-CLE X
Jens Klare, Olaf Saalmann, Helmut Wilden, Andreas R. Brenner
Fraunhofer Institute for High Frequency Physics and Radar Techniques FHRNeuenahrer Str. 20, 53343 Wachtberg, Germany
email: [email protected]
ABSTRACT
Several applications need imaging sensors for environmen-
tal monitoring which can continuously observe an area in a
24/7 mode independently from the weather and other atmo-
spheric obscuration like dust and smoke. Imaging MIMO
radar fulfills these requirements and enables the opportu-
nity of low-cost and robust imaging systems by synthesizing
many virtual antennas out of just a few real ones. MIRA-CLE
is a fully configurable and expandable experimental MIMO
radar in Ka-band while MIRA-CLE X works in X-band and
is intended to be a low-cost experimental system for long
range applications. This paper presents both MIMO radar
systems and shows and discusses first imaging results of
MIRA-CLE X.
Index Terms— MIMO, Multiple-Input Multiple-Output,
MIMO radar, MIMO SAR, Imaging radar
1. THE PRINCIPLE OF IMAGING MIMO RADAR
MIMO (Multiple-Input Multiple-Output) radar is a quite new
but constantly growing research field which offers some es-
sential advantages compared to usual imaging techniques like
SAR. Unlike SAR systems which need for imaging always
an aspect angle change (usually achieved by a motion) be-
tween the sensor and the scene to be observed, MIMO radars
can be used also stationary to provide a continuous moni-
toring (24/7) of the scene of interest (e.g. for rock glaciers,
glacier tongues, snow and debris avalanches, hillside slides,
and mining areas). Although radars with phased arrays anten-
nas could be used for stationary applications, they need a very
large number of antenna elements to achieve a reasonable im-
age quality and resolution. The use of a mechanical reflector
antenna for a pixel by pixel scanning of the scene would need
a large reflector for a reasonable spatial resolution and suffers
by high maintenance costs due to the fragile mechanics. The
aforementioned drawbacks can be smartly bypassed by using
MIMO radars.
𝑁𝑇𝑋 transmit antennas and 𝑀𝑅𝑋 receive antennas are
arranged in a special way that all 𝑁𝑇𝑋 /𝑀𝑅𝑋 pairs form a
fully and regularly distributed virtual antenna array of 𝐼𝑣𝑖𝑟𝑡 =
𝑁𝑇𝑋𝑀𝑅𝑋 virtual elements [1] [2]. Each virtual antenna ele-
ment is located at the center of gravity of one 𝑁𝑇𝑋 /𝑀𝑅𝑋 pair.
One essential advantage compared to phased array systems is
the strongly reduced number of real antenna elements. For
example, instead of using 256 antennas in a classical man-
ner, a MIMO radar with the same array length needs only
16 transmit and 16 receive antennas which reduces costs and
weight. The weight reduction is an important benefit for mo-
bile applications in difficult environmental areas and it makes
it even possible to integrate a 3D imaging MIMO radar in a
small UAV (Unmanned Aerial Vehicle) [3]. With a special
arrangement of the real antennas, one can even double the az-
imuth resolution compared to a phased array system of the
same size [3].
The assignment of each TX antenna to each RX antenna
can be done in 4 different levels. Level 1 uses a time multi-
plexing schema for each TX and each RX antenna to assign all
permutations within one cycle. The effective pulse repetition
frequency is given by 𝑃𝑅𝐹𝑒𝑓𝑓 = 𝑃𝑅𝐹/(𝑁𝑇𝑋𝑀𝑅𝑋). Level
2 uses a switching schema only for the TX antennas and re-
ceives with all RX elements together resulting in 𝑃𝑅𝐹𝑒𝑓𝑓 =𝑃𝑅𝐹/𝑁𝑇𝑋 . Level 3 uses ℱ disjunct frequency bands while
level 4 uses 𝒲 different orthogonal waveforms to enable a
transmission with several or all TX antennas at the same time
[4]. The effective 𝑃𝑅𝐹 can be increased up to 𝑃𝑅𝐹𝑒𝑓𝑓 =𝑃𝑅𝐹 . Level 2-4 increase also the 𝑆𝑁𝑅 (Signal-to-Noise Ra-
tio) for a given coherent processing interval.
The signal model for MIRA-CLE and MIRA-CLE X can
be found in [5]
2. THE EXPERIMENTAL SYSTEMS MIRA-CLE
The Ka-Band system MIRA-CLE is a fully configurable and
expandable MIMO radar working at a center frequency of
36 GHz. This frequency band was chosen in oder to realize
a compact and mobile imaging radar system with an antenna
length of just 50 cm in the basic configuration. This configu-
ration consists of 16 TX and 16 RX vivaldi antennas resulting
in 256 virtual antenna elements. The bandwidth is 500 MHz.
Due to its reconfigurable concept, the bandwidth can be in-
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creased to several GHz and the number of TX and RX anten-
nas can be expanded for a higher spatial resolution in cross
range. In order to increase the 𝑆𝑁𝑅 and the effective 𝑃𝑅𝐹 ,
the MIMO levels 3 (frequency diversity) and 4 (waveform di-
versity) can be applied. Figure 1 shows the model of MIRA-
CLE with the antenna distribution of the basic configuration.
Fig. 1. Antenna configuration of the Ka-Band MIMO radar
MIRA-CLE. The transmit antennas are packed at both sides
of the thinned receive array.
3. THE EXPERIMENTAL SYSTEM MIRA-CLE X
The X-Band system MIRA-CLE X works at a center fre-
quency of 9.45 GHz with a bandwidth of 1 GHz. It uses 16
TX sector horn antennas and 14 RX patch antenna columns
which result in 224 virtual antenna elements and a real an-
tenna size of about 2 m. The radiated pulse power amounts
to 2 W in the basic stage of extension and will be further in-
creased up to about 10 W for larger range applications. The
schematic antenna distribution is superimposed to a photo of
the MIMO antenna (Figure 2).
Fig. 2. Antenna configuration of the X-Band MIMO radar
MIRA-CLE X
3.1. Experimental Setup
MIMO level 1 was used in order to get all TX/RX combina-
tions. For this, one TX antenna was activated for 𝑁𝑅𝑋 pulses
Pulse length 0.5𝜇s
Receive window length 4.1𝜇s
Slant range 615 m
Sampling rate 8 GS/s
Table 1. System settings for the trials
Fig. 3. Range compressed data for each virtual antenna
and the receiver was switched successively from one RX an-
tenna to the other from pulse to pulse. This switching schema
was repeated with all transmit antennas (called a ’cycle’) re-
sulting in 224 combinations. Several cycles were conducted
consecutively in order to increase the 𝑆𝑁𝑅 during signal pro-
cessing by coherent addition after range compression. Differ-
ent scenes were imaged and analyzed. The system settings
for the different scenes were the same (Table 1).
3.2. Image reconstruction
In a first step, the radar data were range compressed for
each virtual antenna. Afterwards, the range compressed data
were Hilbert transformed and down converted to baseband.
A Hamming window was applied to reduce the side lobes.
Since each TX and each RX channel suffers from different
small delays (e. g. due to slightly different cable lengths), a
time shift correction was applied. Then, the data from sev-
eral cycles were coherently summed up to increase the SNR.
Figure 3 shows the resulting range profiles for the different
virtual antennas for one cycle. After pre-processing, two
different processing approaches were used for focusing in
cross-range. The first approach uses a beam forming opera-
tion for each range/angle cell to sum up the contributions for
each virtual antenna after phase correction. These data were
transformed afterwards to a Cartesian grid [5]. In the second
approach, a back projection algorithm was used to project the
data directly to a Cartesian coordinate grid. The calculations
are more time consuming compared to the beamforming ap-
proach. Due to the limited space of this paper, we present
only the results of the back projection approach.
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3.3. Experimental results
3.3.1. Radar image of a small village
A scene in the rhine valley at Bonn-Mehlem was chosen (see
Figures 4a and 4b) in order to image an area containing a
small village and several trees. The radar was placed on top
of a small hill at a height about of 146 m and pointed towards
the center of the village with a depression angle of 15 ∘. The
(a) Radar view from the hill
(b) Ortho photo ( c⃝ Landesvermessungsamt NRW)
Slant Range [m]
Cro
ss ra
nge
[m]
Back projection method [dB]
100 200 300 400 500 600
−200
−150
−100
−50
0
50
100
150
200−40
−35
−30
−25
−20
−15
−10
−5
0
(c) MIMO radar image of the village
Fig. 4. MIMO radar trials of a small village
lowest part of the imaged scene has a height of about 60 m and
is hidden in the radar shadow. Figure 4c shows the final radar
image. A comparison of the radar image with the photo from
the viewpoint of the radar and with the ortho photo shows a
very well agreement. Right in front of the radar at about 80 m
were some trees which are clearly visible and distinguishable
in the radar image (’A’ in the ortho photo). A dominant row
of trees is visible between a range of 150 m and 200 m and a
cross-range of -10 m to -70 m (’B’ in the ortho photo). The
farthest structure in the radar image is a long tree row for a
range between 450 m and 600 m (’D’ in the ortho photo).
3.3.2. Radar image of a big building
The imaged scene consists of the main building of the univer-
sity Bonn with two dense tree rows at both sides of a large
meadow and some small hedges and street lamps in front of
the building (see Figure 5a). A corner reflector was placed
on the meadow in front of the building (see inset of Figure5a). The tree rows at both sides, the building, and the cor-
(a) Radar view towards a building
Slant range [m]
Cro
ss ra
nge
[m]
Back projection method [dB]
100 150 200 250 300
−100
−50
0
50
100 −50
−40
−30
−20
−10
0
(b) MIMO radar image
−15 −10 −5 0 5 10 15
−50
−40
−30
−20
−10
0
Angle [°]
[dB
]
Corner reflector
Real dataTheoretical
−2 0 2
−5
0
(c) Return of the corner reflector
Fig. 5. MIMO radar trials of a big building
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ner reflector are clearly visible in the radar image (see Figure5b). Also the two towers on the back side of the building are
visible at a range of about 330 m. The scatterer right in front
of the building belong mainly to hedges and street lamps. The
small dots on the meadow are reflections from people and bi-
cycles. Figure 5c shows the return of the corner reflector
(blue). A Taylor tapering was used during cross range com-
pression to get a maximum sidelobe level of −30 dB. The the-
oretical point spread function is plotted in red. One can ob-
serve a very good agreement between the theoretical and the
measured cross range resolution. The higher side lobe levels
of the real data are caused by the background signal of the
scene and the system noise.
3.3.3. Radar image of a vineyard and a rock
The radar was placed on the bottom of a vineyard and a rock
(see Figure 6a). Two paths cross the vineyard and some trees
and bushes are visible around the rock. Tow foils made of dif-
ferent material and size were fixed at a wall behind the lower
path. The scene can be well recognized in the MIMO radar
image (see Figure 6b). The two pathes and the rock structure
are clearly visible. The right foil made out of thick metal pro-
duces a strong radar return (see Figure 6c). The smaller left
foil consisting of plastic with a thin metal surface is fainter
but also visible in the radar image.
4. CONCLUSION AND OUTLOOK
The results of the imaging MIMO radar demonstrator MIRA-
CLE X have successfully demonstrated the MIMO princi-
ple applied to radar. Applications which require a stationary
imaging radar for a continuously monitoring of a scene bene-
fit from the MIMO technique. Especially the field of change
detection (e. g. warning system for hill slides) could profit
from MIMO radars.
In a next step, MIRA-CLE X will be expanded by more
powerful amplifier to increase the 𝑆𝑁𝑅 and to allow imag-
ing over larger distances. The Ka-Band system MIRA-CLE
will be used for near range applications and to demonstrate
waveform and frequency diversity for imaging MIMO radars.
5. REFERENCES
[1] J. H. G. Ender and J. Klare, “System architectures and
algorithms for radar imaging by MIMO-SAR,” in IEEERadarCon 2009, Pasadena, USA, May 2009, pp. 1–6.
[2] J. Li, P. Stoica, and X. Zheng, “Signal synthesis and re-
ceiver design for MIMO radar imaging,” Signal Process-ing, IEEE Transactions on, vol. 56, no. 8, pp. 3959–3968,
Aug. 2008.
[3] J. Klare, M. Weiss, O. Peters, A. R. Brenner, and J. H. G.
Ender, “ARTINO: A new high resolution 3D imaging
(a) Radar view towards a vineyard and a rock
Slant range [m]
Cro
ss ra
nge
[m]
Back projection method [dB]
100 150 200 250 300
−150
−100
−50
0
50
100
150 −50
−40
−30
−20
−10
0
(b) MIMO radar image
(c) Identification of foils in the vineyard
Fig. 6. MIMO radar trials of a vineyard, a rock, and foils
radar system on an autonomous airborne platform,” in
Proc. IEEE International Conference on Geoscience andRemote Sensing Symposium IGARSS 2006, Denver, USA,
July 2006, pp. 3842–3845.
[4] J. Klare, “Digital beamforming for a 3D MIMO SAR
- improvements through frequency and waveform diver-
sity,” in Proc. IEEE International Geoscience and Re-mote Sensing Symposium IGARSS 2008, Boston, USA,
July 2008, vol. 5, pp. V 17–20.
[5] J. Klare and O. Saalmann, “MIRA-CLE X: A new imag-
ing MIMO-radar for multi-purpose applications,” in 7thEuropean Radar Conference, EuRAD 2010, September
2010, accepted.
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