A New Automatic Target Recognition Scheme Based on Model Simulation and Structured Learning

In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its application in image analysis and pattern recognition, due to its good characteristics of high recognition rate, robustness to corruption and occlusion, and little dependence on the features selection etc. However, sufficient training samples are always required by the sparse representation method for the effective recognition. In practical applications, it is generally difficult to obtain sufficient training samples of the test targets, especially non-cooperative targets. So the key issues in the effective automatic target recognition (ATR) based on the sparse representation are to obtain sufficient training samples in different scales, angles, and different illumination conditions, and to construct an overcomplete dictionary with discriminative ability. In this paper, a novel sparse representation-based scheme is proposed for the automatic target recognition in the real environment, in which the training samples are drawn from the simulation models of real targets and the overcomplete dictionary is trained using structured sparse learning method. The experimental results show that the proposed method is effective for the automatic target recognition in the practical application, especially, where the desired features of the sparse representation method are kept.