Sparsity-motivated automatic target recognition

We present an automatic target recognition algorithm using the recently developed theory of sparse representations and compressive sensing. We show how sparsity can be helpful for efficient utilization of data for target recognition. We verify the efficacy of the proposed algorithm in terms of the recognition rate and confusion matrices on the well known Comanche (Boeing–Sikorsky, USA) forward-looking IR data set consisting of ten different military targets at different orientations.