Wide area traffic monitoringwith the PAMIR system

D. Cerutti-Maori, J. Klare, W. Burger, A.R. Brenner, J.H.G. EnderForschungsgesellschaft fur Angewandte Naturwissenschaften e.V. (FGAN)

Neuenahrer Str. 20, 53343 Wachtberg, Germany

Email: cerutti@fgan.de

Abstract—This paper presents some Scan-MTI results, whichwere obtained with the SAR-GMTI system PAMIR developed atFGAN-FHR. The Scan-MTI mode was designed to rapidly mon-itor wide areas for ground moving targets. The scan operationenables detection of targets from different aspect angles with ahigh revisit rate. This mode is particularly adapted to performan efficient traffic monitoring, as well.

I. REQUIREMENTS FOR TRAFFIC MONITORING

Radar systems are especially suited for traffic monitoring

due to their unique capability to detect and locate mov-ing objects at all-weather conditions during day and night.

However, several requirements are necessary to achieve an

efficient traffic monitoring. The system should allow a widearea surveillance of moving objects. Moreover, the revisit rate

has to be high enough to perform an efficient tracking of the

detected objects. Another important requirement is the abilityto detect the same moving object from different aspect angles.

In [1] and [2] it was shown that the RCS (Radar Cross Section)

of an automobile strongly depends on the aspect angle and canvary up to 30 dB. If an object cannot be seen form a particular

angle, it might be possible to detect this object at other aspect

angles. Furthermore, to estimate the azimuth positions of theobjects accurately, the azimuth beamwidth of the radar antenna

has to be narrow. In order to fulfill all these requirements, ascan operation of the antenna beam has to be implemented.

This is achieved by steering the antenna from one azimuth

angle to another after a predetermined number of transmittedpulses (bursts). Thus, different parts of the ground are scanned

successively and a wide area can be monitored.

However, velocity ambiguities may occur with the useof large scan angles for a distinct PRF (Pulse Repetition

Frequency). In addition, occurring blind velocities may impedethe detection of objects moving at such radial velocities. Both

problems can be solved either by changing the PRF between

two bursts of pulses or by changing the radar wavelength [3].

II. THE SCAN-MTI MODE OF PAMIR

The SAR-GMTI system PAMIR (Phased-Array Multifunc-

tional Imaging Radar) [4], developed at FGAN-FHR, is a mul-

tifunctional X-band radar, which combines a high-resolutionimaging radar with an efficient GMTI (Ground Moving Target

Indication) surveillance radar. In order to demonstrate widearea GMTI, an efficient Scan-MTI mode, which fulfills all the

aforementioned capabilities, was implemented in PAMIR. The

used phased-array antenna allows a scan capability of ±45◦.Currently, the receive antenna with a length of 65 cm is divided

into three receive channels. In its final development stage,

PAMIR will operate with a 4.25 m long antenna with five

parallel receive channels for efficient GMTI. The capability

to detect moving objects is further improved by transmittingbursts in five different frequency bands of PAMIR one by one

for each azimuth look direction. With this mode, PAMIR can

monitor a region of about 200 km2 per scan depending on thesystem parameters (for a mean range of 20 km with a scan

angle of ±25◦).

To validate the performance of the Scan-MTI mode of

PAMIR, a controlled experiment was conducted during a flight

campaign in November 2003. PAMIR, which was mountedon a Transall C-160, flew over a scene in which cooperative

targets and targets of opportunity were moving. This paper

focuses on the targets of opportunity, which were numerousin the explored scene. It is shown that an efficient traffic

monitoring of road networks in both, rural and urban areas

can be achieved.

III. SCAN-MTI ALGORITHM

An overview of the algorithm developed to process the data

acquired with this mode was presented in [3] and [5]. The

signal processing procedure is organized as follows. For eachantenna look direction the data of the five frequency bands are

first processed independently from each other. The processingincludes a data calibration [6], the clutter cancellation, and

the estimation of the azimuth position of the detected targets.

Afterwards, the detection maps of the five frequency bandsare combined incoherently and the position of each detected

target is computed in the earth reference frame. The results are

finally transferred to a tracking algorithm developed at FGAN-FKIE. In the following, we focus on some specific points of

the algorithm, which were improved.

A. Clutter filtering

The clutter cancellation algorithm is based on STAP (Space-

Time Adaptive Processing) [7], since a joint processing in

space and time is required to separate the echoes of the movingtargets from the clutter. In order to avoid the suppression of

moving targets, the clutter filtering is done in the Doppler

domain and combines an adaptive filter with a theoreticalfilter. Due to the low number of pulses, which are jointly

processed, it can be assumed that a moving object remains inone Doppler cell. The coefficients of the clutter filter (derived

from the sample covariance matrix [8]) are computed from an

adjacent Doppler cell and applied to the considered Dopplercell after a phase and amplitude correction derived from the

clutter subspace [8].

B. Estimation of the azimuth position

Three different methods (monopulse, adaptive monopulse,

and maximum likelihood) for the estimation of the azimuth po-sition were implemented [9]. All these techniques compute the

angle difference between the line-of-sight to the target and the

look direction of the antenna. However, the look direction ofthe antenna may slightly vary between the different frequency

bands. If these squint angle variations are not compensated,

they worsen the estimated target positions, as illustrated inFigures 1(a), 1(b), 1(d), and 1(e).

A possibility to compensate this squint angle variation (ifnot corrected by the system itself) is the following. Let fd(n)be the Doppler centroid of the n-th frequency band. It can be

written as

fd(n) =2

λn

vplat cos(θ) cos(ϕ0 + α + δϕn) (1)

with vplat the platform velocity, θ the depression angle, andλn the center wavelength. ϕ0 denotes the look angle of the

antenna, α the mean squint angle, which is common to all the

frequency bands, and δϕn the squint angle variation betweenthem. For the reference frequency band nref , we assume that

δϕnref= 0. The common squint angle α can be expressed by

α = acos(λnref

fd(nref )

2vplat cos(θ)) − ϕ0 (2)

The look angle shift between the n-th frequency band and thefrequency band nref can be then estimated as

δϕn = acos(λnfd(n)

2vplat cos(θ)) − acos(

λnreffd(nref )

2vplat cos(θ)) (3)

The squint angles δϕn have to be computed for each look

direction. An averaging over the scans with the same look

direction allows to get a better estimation accuracy.

C. Post detection

In order to combine the data of the five frequency bands,a scaling has to be applied. This is due to the fact that a

target with radial velocity vrtappears with different Doppler

frequencies fdt(n) = −

2λn

vrtfor each frequency band be-

cause of the different center frequencies. As a consequence,

the radial velocity resolution differs from one frequency band

to another

δvr(n) =λn

2δfd (4)

δfd denotes the resolution of a Doppler cell, which is the same

for all the frequency bands, since the PRF and the number of

pulses Npulse are identical. The phase of the azimuth chirpfor the n-th frequency band is given by

Φ(n) = −4π

λn

(Rt + ∆Rt(n)) (5)

with Rt the range to the target which is constant for all

frequency bands. ∆Rt(n) ≈ −fd(n)λnNpulse(n−1)

2PRFdenotes the

range walk between the data of the first and the n-th frequency

band. After range walk correction, a scaling of the azimuthchirp has to be done so that all the radial velocity resolutions

match. This post detection step also allows to resolve the blind

Center frequency 9.45 GHzTotal bandwidth 1.8 GHzNumber of sub-bands 5Number of receive channels 3Azimuth scan angle 70◦-110◦

Azimuth beam width 2.8◦

Elevation beam width 12.5◦

Number of dwells per scan 32Number of bursts per dwell 5Number of pulses per burst 128PRF 6 kHzBandwidth of the transmitted signal 20 MHz

Revisit time 113 km2/3.4 sMean slant range 17 km

TABLE IPARAMETERS OF THE SCAN-MTI EXPERIMENT (2003)

velocities and the velocity ambiguities, since they are different

for each frequency band as described in [3].

IV. EXPERIMENTAL RESULTS

Figure 2 presents the results of the conducted experimentafter data processing. The parameters of the experiment are

shown in Table I. The detected moving objects are indicatedby points. The 2D error bars indicate the uncertainty of their

positions. They were calculated using error propagation and

vary slightly over the whole scene. The georeferencing ofthe targets was done without taking into account the Digital

Elevation Model of the scene. All plotted targets were detected

in at least three out of the five frequency bands. During the datatake, the platform flew in North-East direction as indicated in

Figure 1(f). The time of the data acquisition is coded in colors

from blue to red. The duration was of about 120 s, whichcorresponds to 35 scans with 32 look directions each.

The estimation of the azimuth positions of the objects wasdone with the adaptive monopulse method. Contrary to expec-

tations, the maximum likelihood method gave slightly inferior

results. This can be due to the fact that for this experiment theantenna patterns were not sufficiently known for each look

angle and frequency band. Thus, the antenna patterns were

estimated from the data (see Figure 1(c)), which may result ininaccurate estimations with the maximum likelihood method.

However, with the monopulse methode the estimation accuracy

of the azimuth angle worsens with an increasing angle betweenthe direction to the target and the line of sight of the antenna

[9]. This can explain in some cases the inaccurate estimation

of the target positions.The estimation accuracy in range direction is given by the

range resolution. In azimuth direction, the position uncertaintyis the sum on the uncertainties on the estimation of the angle

between the antenna look direction and the target direction

(given by the Cramer-Rao bounds) and on the estimation ofthe squint angles. Due to the high PRF and the low number of

pulses, only a coarse estimation of the Doppler centroid and

thus of the squint angles can be achieved. The position errorof the platform (given by GPS) has to be considered also as

well as a time shift between the data take time and the GPSrecording (synchronization error). Due to the low depression

angle of 8◦, an uncertainty on the platform altitude corre-

sponds to a large range shift. Using all uncertainty sources,an estimation uncertainty of about ±160 m in longitude and

±90 m in latitude was obtained for the mean range.

Longitude [°]

Latitu

de [°

]Detections in Scan 08 Direction 28 (Color: Time [s]) [Lo3]

9.46 9.47 9.48 9.49 9.5 9.51 9.52 9.5348.4

48.405

48.41

48.415

48.42

48.425

48.43

48.435

48.44

10

30

50

70

90

110

(a) Detected targets with the frequency band 3

Longitude [°]

Latitu

de [°

]

Detections in Scan 08 Direction 28 (Color: Time [s]) [Lo4]

9.46 9.47 9.48 9.49 9.5 9.51 9.52 9.5348.4

48.405

48.41

48.415

48.42

48.425

48.43

48.435

48.44

10

30

50

70

90

110

(b) Detected targets with the frequency band 4

−3000 −2000 −1000 0 1000 2000−35

−30

−25

−20

−15

−10

−5

0Estimated antenna pattern [dB] −Scan 8, Direction 28, Lo 3−

Doppler [Hz]

Am

plit

ude [dB

]

Channel 1Channel 2Channel 3

(c) Estimated antenna characteristics from thedata

Longitude [°]

Latitu

de [°

]

Detections in Scan 08 Direction 28 (Color: Time [s])

9.46 9.47 9.48 9.49 9.5 9.51 9.52 9.5348.4

48.405

48.41

48.415

48.42

48.425

48.43

48.435

48.44

10

30

50

70

90

110

(d) Detected targets after averaging the estimatedazimuth positions over the five frequency bandswithout correcting the squint angles between them

Longitude [°]

Latitu

de [°

]

Detections in Scan 08 Direction 28 (Color: Time [s])

9.46 9.47 9.48 9.49 9.5 9.51 9.52 9.5348.4

48.405

48.41

48.415

48.42

48.425

48.43

48.435

48.44

10

30

50

70

90

110

(e) Detected targets after averaging the estimatedazimuth positions over the five frequency bands andcorrecting the squint angles between them

Longitude [°]

La

titu

de

[°

]

Platform position (Color: time [s])

9.5 9.55 9.6 9.65 9.748.28

48.3

48.32

48.34

48.36

48.38

48.4

48.42

48.44

48.46

48.48

10

30

50

70

90

110

(f) Position of the antenna platform during thedata take

Fig. 1.

During the experiment, three different convoys of coop-erative targets were moving in the scene. Each group was

equipped with GPS. The convoys are indicated with C1,

C2, and C3 in Figure 2. Their positions could be correctlydetermined in comparison to the ground truth given by the

processing of their GPS tracks. A more detailed analysis about

their velocities is presented in [3].

The other moving objects are targets of opportunity. Thescene contains several villages connected by roads. The pro-

cessing results show much traffic on the roads between the

villages as well as in the villages themselves. This highlightsthe traffic situation during a particular time over a wide area.

Due to the antenna scan, it is possible to detect the same target

at different times which is visible as the dense colored trafficflow on the roads. This behavior can be better seen when a

single automotive was monitored over a long time withouta superposition with other moving objects. An example is

labelled with P1. The color change from blue over green and

yellow to red which marks likely the motion of a single object.

One can see also some other features beside the movingobjects. A target of interest (P2) indicates a tower on the top

of a hill. Since a height can be detected as a moving target with

a STAP processor, it may explain the numerous detections inthis area. Another object of interest (P3) marks a wind turbine.

At the day of the data take, it may have rotated, which would

explain the detections in this region.

V. CONCLUSION

New GMTI results achieved with the PAMIR system were

presented. It was shown that targets of opportunity could be

well detected and positioned. The presented signal processingapproach has improved the target detection capabilities as

well as the target positioning. The Scan-MTI mode allows

to monitor the dense traffic on roads between villages as wellas in the villages themselves. This shows that the Scan-MTI

mode is very capable for a wide area traffic monitoring.

REFERENCES

[1] D. Hounam et al.: An autonomous, non-cooperative, wide-area TrafficMonitoring System using space-based Radar (TRAMRAD) in Proceed-ings of IGARSS 2005, Seoul, Korea, July 2005.

[2] G. Palubinskas, H. Runge, P. Reinartz: Radar signatures of roadvehicles: airborne SAR experiments, in Proceedings of SPIE Vol. 5980,Bruges, Belgium, September 2005.

[3] D. Cerutti-Maori, W. Burger, J.H.G. Ender, A.R. Brenner: Wide AreaSurveillance of Moving Targets with the SAR/GMTI System PAMIR, inProceedings of EUSAR 2006, Dresden, Germany, May 2006.

[4] A.R. Brenner, J.H.G. Ender: Demonstration of advanced reconnaissancetechniques with the airborne SAR/GMTI sensor PAMIR, IEE Proceed-ings Radar, Sonar and Navigation, Vol. 153, 2006.

[5] D. Cerutti-Maori, W. Burger, J.H.G. Ender, A.R. Brenner: ExperimentalResults of Ground Moving Target Detection achieved with the Multi-channel SAR/MTI system PAMIR, Proceedings of the European Mi-crowave Association, Vol. 2, Issue 2, June 2006, pp. 122-127.

[6] J.H.G. Ender: The airborne Experimental Multi-Channel SAR-SystemAER-II, in Proceedings of EUSAR 1996, Konigswinter, Germany, May1996.

[7] R. Klemm: Principles of Space-Time Adaptive Processing, IEEPublishers, London, UK, 2002.

[8] J.H.G. Ender: Space-time processing for multichannel synthetic apertureradar, Electronics & Communication engineering journal, pp. 29–38,February 1999.

[9] U. Nickel: An overview of generalized monopulse estimation, IEEEAerospace and Electronic Systems Magazine, Vol. 21, Issue 6, Part 2,June 2006, pp. 27- 56.

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26

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1357911

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itu

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[°]

/

Latitude [°] /

[km

]

[km]

A

ccu

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late

d d

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s o

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can

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irecti

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Co

lor:

tim

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s])

− A

rea:

26km

×13km

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9.5

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.69

.65

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.38

48

.4

48

.42

48

.44

48

.46

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C3

P2

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Fig. 2. Accumulated detections of moving targets over 35 scans (Map PCMAPc©)