Multi-view radar target recognition based on multitask compressive sensing

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.

Keywords:

• radar automatic target recognition
• multiple views
• multitask compressive sensing
• high-range resolution profile

Acknowledgement

The authors wish to thank the reviewers for their helpful suggestions. The authors also thank Dr Shihao Ji for sharing the Matlab code.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research work was supported by the National Natural Science Foundation of China [grant number 61471370], [grant number 61401479].