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Joint classification of complementary features based on multitask compressive sensing with application to synthetic aperture radar automatic target recognition

Abstract. We propose a synthetic aperture radar (SAR) automatic target recognition (ATR) method by jointly classifying three complementary features based on multitask compressive sensing (MtCS). The principal component analysis features, elliptical Fourier descriptors and the azimuthal sensitivity image, are extracted or constructed to describe the intensity distribution, target shape, and electromagnetic characteristics of the original SAR images, respectively. The three features describe the original SAR image from different aspects, thus their joint use can provide more discrimination for distinguishing different classes of targets. Afterward, the three features are jointly classified based on MtCS, which can properly represent individual tasks, and also exploit their inner correlations. Therefore, it is promising that the discriminability of different features can be better exploited to improve the ATR performance. Extensive experiments are conducted on the moving and stationary target acquisition and recognition dataset under both the standard operating condition and several typical extended operating conditions, i.e., configuration variance, large depression angle variance, noise corruption, and partial occlusion. The results demonstrate the effectiveness and robustness of the proposed method in comparison with several state-of-the-art SAR ATR methods.