A NEW METHOD FOR MOVING TARGET DETECTION IN SAR

IMAGERY

Murat Hakan Yusufoglu, Mesut Kartal

Informatics Institute, Istanbul Technical University, Istanbul, Turkey

hakan.yusufoglu@gmail.com, kartalme@itu.edu.tr

Keywords: Synthetic Aperture Radar, Moving Target Detection, Shear Averaging, Sub-aperture Processing.

Abstract: This paper presents a new algorithm for detection and parameter estimation of moving targets in synthetic

aperture radar (SAR) images. The proposed algorithm is capable of detecting targets moving in both range

and azimuth directions, and also motion parameter estimation of the detected targets. This new algorithm

uses “sub-aperture processing” and “shear averaging algorithm" for detection of range and azimuth

direction movements respectively. Detection algorithm is processed in range and azimuth directions

independently; therefore, algorithm is suitable for parallel processing. In addition to this property, detection

performance and motion parameter estimation accuracy is high because of the non-sequential processing of

range and azimuth motion detection. Computer simulations gives promising results of detecting moving

targets in all directions and also extracting motion parameters of the detected target.

1 INTRODUCTION

Synthetic aperture radar technology brings new

developments in modern world. Today, numerous

SAR applications are seen in very different areas.

These applications include environmental research,

scientific, civilian and military purposes. The main

application area of SAR is aimed for detailed

imaging of specific earth terrains. By using SAR

imagery technology, any terrain image can be

collected easily. These detailed images are useful for

researching terrain properties.

Detection and motion parameter estimation of

moving targets within the observed region is also

possible by using the SAR images. Information of

detected moving objects can be used in very

different applications, such as monitoring traffic

flow (Palubinskas and Runge, 2008), observation of

military field, tracking of a specific moving target

and motion parameter estimation of the targets.

Different algorithms are proposed for detection

of moving targets. They are detecting moving targets

by using displaced phase centre antenna (Jung, 2009,

and Qin, Zhang and Dong, 2006), along track

interferometry (Kohlleppel and Gierull, 2008),

single-channel radar processing (Li, Xu, Peng and

Xia, 2006, Liu, Yuan, Gao and Mao, 2007, Kirscht,

1998, and Kirscht, 2002), and focusing algorithms

(Fienup, 2001).

As discussed by Kirscht (1998), moving targets

are appeared defocused or at wrong positions

depending on the direction of the target motion

within the SAR image. If a target moves in azimuth

direction, motion causes blurring effect in azimuth

direction, and if it moves in range direction, motion

also causes a displacement in azimuth direction or

for a higher range velocity of the target, it even

disappears (Kirscht, 1998 and Fienup, 2001). Many

algorithms are evaluated based on these blurring and

displacement effects on the images to detect moving

targets.

Our proposed algorithm is capable of detecting

targets moving not only in azimuth direction, but

also in range direction. “Sub-aperture processing”

and “shear averaging algorithm” is used to detect

moving targets in range and azimuth directions

respectively. Detection algorithm for range direction

and for azimuth direction can be processed

separately. This property gives the advantage of

using parallel processing techniques. Therefore

range and azimuth movement processes can be

completed simultaneously. Also, independent

motion detection processing of range and azimuth

movements gives more accurate detection results.

Yusufoglu M. and Kartal M.

A NEW METHOD FOR MOVING TARGET DETECTION IN SAR IMAGERY.

DOI: 10.5220/0005414200760079

In Proceedings of the First International Conference on Telecommunications and Remote Sensing (ICTRS 2012), pages 76-79

ISBN: 978-989-8565-28-0

2 THE PROPOSED ALGORITHM

In the proposed algorithm, single-channel SAR

system is considered and spotlight mode raw data is

used. A good clutter cancellation is applied before

starting the algorithm steps.

Motion effects on the SAR images as discussed

by Kirscht (1998) and Fienup (2001) are used in the

algorithm. Target motion in azimuth direction,

causes smear effect due to the motion induced phase

errors. On the other hand, target motion in range

direction causes displacement of the targets in

azimuth direction.

After all moving targets has been detected

number of detected targets, their velocities and

movement directions are reported. Range and

azimuth movement detection processes are detailed

in the following subsections.

2.1 Range Direction Movement

Detection

In the proposed algorithm, range direction

movement is detected by using sub-aperture

processing (Franceshetti and Lanari, 1999). Raw

data is divided in two equal blocks across the

azimuth direction, and two SAR images are formed.

This process provides looking to the same observed

region in two different time intervals. The first

image contains data from beginning to the divided

position of the antenna. Therefore, an image is

generated for “t” time position of the antenna. The

second image contains data from the divided

position to the end position of the antenna. So,

second image is generated for “t + 1” time position

of the antenna.

After generation of the images, these two images

are overlapped with the help of the SAR system

parameters. By taking the difference between the

two overlapped images, stationary targets will be

disappeared and only moving targets within the

observed region are detected.

After moving target has been detected, position

difference of the target between the first and the

second images gives the range direction movement

information of the target. This information is used to

extract range direction motion parameters.

2.2 Azimuth Direction Movement

Detection

Shear averaging algorithm is used for detecting

moving targets in azimuth direction. There are

numerous algorithms for detecting azimuth

movement. But shear averaging algorithm is chosen

for its sensitivity to the azimuth component of

velocity, providing very fast calculation, higher

order phase errors detection ability, and not

requiring a prominent point scatterer on the target

(Fienup, 2001).

In the algorithm, whole image is divided into

small patches. By processing each patch, moving

targets can be detected by using “shear averaging

algorithm” detailed by Fienup (1989). If good clutter

cancellation is applied at the beginning of the

algorithm, only targets will be stayed in the image.

So, moving targets can accurately be detected with

very low false alarm rate.

In the proposed algorithm, following steps are

used to detect azimuth motion.

a. Divide the image into patches.

b. Take a patch data, g(u,v).

c. Calculate G(u,v) by taking azimuth FFT of

g(u,v).

d. Calculate shear averaged quantity.

e. Calculate phase error estimate in azimuth

coordinate.

f. Make phase correction.

g. Take inverse Fourier of corrected data.

h. Calculate standard deviation of the phase

error.

i. Compare standard deviation value with the

threshold value to detect moving target.

j. If a moving target is detected, find the

azimuth velocity of the target by using the

system model.

Threshold value could be calculated by

processing either whole image or only patch data.

The whole image processing gives a fixed threshold

value. But, for good detection results in simulations,

dynamic threshold value is calculated for each patch.

In “jth” step of the algorithm, azimuth velocity is

calculated by finding the displacement of position of

maximum amplitude within the unfocused and

focused images. From simulation results, a

relationship between real target velocity and

detected target velocity is extracted and shown in

Figure 1. This relation is used as a reference system

model for the velocity estimation of the detected

targets.

By combining the range and azimuth direction

detection results, the real movement direction and

velocity of the target is calculated.

A New Method for Moving Target Detection in SAR Imagery

Figure 1: System model, extracted from simulation results,

is used to find the azimuth velocity estimation of moving

targets in azimuth direction.

3 SIMULATION RESULTS

The performance of the proposed algorithm is tested

with Matlab simulation. In the simulation scenario,

moving and stationary targets are put within the

simulation data. The simulated scene used in the

simulations is shown in Figure 2. Simulated SAR

system parameters are given in the Table 1.

Simulated scene contains 3 targets. Only the

rightmost target is non-stationary, and the other two

are stationary. The moving target is marked in the

images shown in Figure 3 and Figure 4. In these

figures, moving targets have a constant velocity of

3.9m/s (14km/h) only in range and azimuth

direction, respectively.

In the simulation scenarios, moving targets with

different velocities between 0.39m/s to 15.6m/s are

considered. All moving targets in the azimuth

direction are detected successfully, and target

velocities are estimated by using the system model

given in Figure 1. On the range velocity detection

process, moving targets with velocities smaller than

1.56m/s couldn’t be detected by using two sub-

apertures. But all other targets and also their

movement direction are detected successfully.

Table 1: Simulation system parameters.

Center Frequency

10 GHz

Pulse duration

1 μs

Radar PRF

200 Hz

Sampling Frequency

180 MHz

Chirp rate

1.5 x 10

Platform velocity

200 m/s

Slant range scene center

10 km

Resolution range

0.3 m

Resolution azimuth

0.3 m

Squint angle

0°

Scene size

200 m x 200 m

SAR image size

512x512 pixels

Number of sub-apertures

Aperture size (Az x Range)

256x512 pixels

Patch size (Az x Range)

128x16 pixels

Moving target velocity

3.9 m/s

Excluding targets with very low range velocities,

the moving targets are detected and separated from

stationary targets by sub-aperture processing. Also,

their motion parameters are extracted. In addition to

range direction detection results, azimuth movement,

its direction and velocity of the target are detected

successfully.

Figure 2: Simulated scene.

0 10 20 30 40

Detected target velocity

Real target velocity

First International Conference on Telecommunications and Remote Sensing

Figure 3: Range-only motion.

Figure 4: Azimuth-only motion.

4 CONCLUSION

A new algorithm for detecting both range and

azimuth motion of moving targets in SAR images is

proposed. The combination of sub-aperture

processing and shear averaging algorithms provides

the detection of the movement in all directions.

Detection algorithm is processed in range and

azimuth directions independently; therefore parallel

processing techniques could be used. By parallel

processing, moving targets can be detected very fast.

Algorithm is capable of not only moving target

detection, but also motion parameter estimation of

the moving targets. Moreover, detection

performance and motion parameter estimation

accuracy is high because of the non-sequential

processing of range and azimuth direction

movement.

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A New Method for Moving Target Detection in SAR Imagery