Compressive feature and kernel sparse coding-based radar target recognition

In this study, the authors exploit the sparse nature of radar targets, and propose a universal, target-oriented `compressive feature´ and kernel sparse coding-based radar target recognition approach via the recent developed compressive sensing theory. Inspired by the visual attention mechanism, pulse contourlet transform is proposed to derive the target-oriented compressive features, and a kernel sparse coding classifier is advanced inspired by the fact that kernel trick can make the features more clustered in higher dimensional space, so resulting in accurate and robust recognition of targets. Some experiments are taken on recognising three types of ground vehicles in the moving and stationary target acquisition and recognition public release database, to compare the performance of the proposed scheme with its counterparts, and the results prove its efficiency.