Abstract
A novel multitask compressive sensing (MtCS)-based method for multi-view radar automatic target recognition is presented in the paper. The sparse representation vectors recovered jointly via MtCS are used as recognition features, and classification is performed according to minimum reconstruction error criterion. Compared to the conventional methods, the proposed method has a significant advantage of exploiting the statistical correlation among multiple views for target recognition. Experiments were conducted using a synthetic vehicle target data-set and the moving and stationary target acquisition and recognition database. The results show that the proposed method achieves promising recognition accuracy, and is robust with respect to noisy observations and complex target types.
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