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: jens.klare@fhr.fraunhofer.de

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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