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International Journal of Remote Sensing Applications Volume 4 Issue 4, December 2014 doi: 10.14355/ijrsa.2014.0404.02
Wide-Angle High Resolution SAR Imaging and Robust Automatic Target Recognition of Civilian Vehicles Deoksu Lim1, Luzhou Xu2, Yijun Sun3, Jian Li*4 Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
1,2,*4
Department of Microbiology and Immunology, The State University of New York, Buffalo, NY 14214, USA
3
lemduck@ufl.edu; 2xuluzhou@ufl.edu; 3yijunsun@buffalo.edu; *4li@dsp.ufl.edu
1
Abstract This paper focuses on wide-angle synthetic aperture radar (SAR) imaging and automatic target recognition of civilian vehicles. A recently proposed hybrid data adaptive method is applied to generate accurate and sparse SAR images of civilian vehicles. We combine projection slice theorem (PST) with 2-D FFT to obtain a more accurate pose estimation than the established PST. Given the so-obtained pose estimates, the horizontal and vertical cumulative-sum-vector (CSV) profiles are utilized to focus the SAR image only on the vehicle of current interest. The corresponding vertical CSV is used as a simple feature for automatic target recognition (ATR). We adopt the local learning based feature selection for ATR. The effectiveness of the entire chain of imaging, pose estimation, feature extraction, and ATR methods is verified using the experimentation results based on the publicly available GOTCHA SAR data set. We demonstrate that the high resolution SAR imaging results in much improved ATR performance compared to the conventional SAR imaging. Keywords SAR; IAA; SLIM; Pose Estimation; PST; ATR; Local Learning Based Feature Selection
Introduction Synthetic aperture radar (SAR) systems have been widely used in military and civilian applications (Jakowatz, 1996). The employment of wide-angle SAR imaging can improve the automatic target recognition (ATR) performance (Dungan et al., 2012). This paper focuses on wide-angle SAR imaging, pose estimation, feature extraction, and automatic recognition of civilian vehicles based on the publicly available GOTCHA data set (Dungan et al., 2012). This data set contains 31 circular orbits of a scene with a diameter measuring 5 km, labeled from 214 to 244 in range. The carrier frequency and the bandwidth of the radar are 9.7 GHz and 600 MHz, respectively. The data set
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contains spotlight extracted phase history data of 33 civilian vehicles and 22 reflectors (Dungan et al., 2012). In order to generate well-focused SAR images, the prominent-point auto-focusing method (Carrara et al., 1995) is used to correct motion-induced errors in the phase-history data. Traditional radar systems assume point scatterers, which might be appropriate for narrow angular apertures. However, the scatterer responses become angle-dependent in the wide-angle case. Therefore, we divide the wide-angle aperture into many narrow angular apertures or sub-apertures and then apply a hybrid high resolution SAR imaging method to obtain the imaging result for each subaperture. This hybrid method involves applying the iterative adaptive approach (IAA) (Yardibi et al., 2010; Glentis and Jakobsson, 2011) first to find accurate and unbiased estimates and then the sparse learning via iterative minimization (SLIM) method (Tan et al., 2011) to obtain sparse imaging results by suppressing sidelobes. We show, via comparison with the standard back-projection (BP) method (Gorham and Moore, 2010), that such hybrid method can provide enhanced image resolution as well as reduced sidelobe levels, while maintaining high accuracy. The final wide-angle image is obtained by non-coherently combining all the sub-aperture images. The high resolution SAR images look good visually, but how will they enhance the ultimate goal of automatic target recognition (ATR)? We will address this question herein. First of all, the inconsistency of the target pose in the wide-angle SAR image can result in ATR performance degradation. It is therefore necessary to eliminate this effect so that the targets in each image have the same pose before target recognition. The shape of the target in each wide-angle SAR image is approximately rectangular with two dominant parallel long edges (straight lines). The pose of the target can be obtained