In this paper, we introduce a novel joint sparse representation based automatic target recognition (ATR) method
using multiple views, which can not only handle multi-view ATR without knowing the pose but also has the
advantage of exploiting the correlations among the multiple views for a single joint recognition decision. We cast
the problem as a multi-variate regression model and recover the sparse representations for the multiple views
simultaneously. The recognition is accomplished via classifying the target to the class which gives the minimum
total reconstruction error accumulated across all the views. Extensive experiments have been carried out on
Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate the proposed
method compared with several state-of-the-art methods such as linear Support Vector Machine (SVM), kernel
SVM as well as a sparse representation based classifier. Experimental results demonstrate that the effectiveness
as well as robustness of the proposed joint sparse representation ATR method.
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