The emerging field of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classification is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features are extracted as a l1-norm sparse problem on the basis of the SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed of training samples, BCS is utilized to design targets classifier. For target classification performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experimental results illustrate the effectiveness and robustness of the proposed approach.
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