Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging

We propose a sample selection method for multiple-input, multiple-output ultra-wideband noise radar imaging using compressive sensing. The proposed sample selection is based on comparing the norm values of candidates among the potential received signal and selecting the largest M samples among N per antenna to obtain selection diversity. Moreover, we propose an adaptive weighting allocation that improves reconstruction accuracy of compressive sensing by maximizing the mutual information between target echoes and transmitted signals. This weighting scheme is applicable to both sample selection schemes, a conventional random sampling and the proposed selection. Further, the weighting allocation with the knowledge of recovery error is proposed for more practical scenarios. Simulations show that the proposed selection and weighting allocation enhance multiple target detection probability and reduce normalized mean square error.