Radar Imaging Based on Compressed Sensing by Random Convolution

Compressed Sensing (CS) theory provides great possibilities for resolving problems associated with traditional high resolution radar, such as high sampling rate, too many data and difficulties of real time processing. Sensing by random convolution is a universally efficient data acquisition strategy and easy to realize. This paper focuses on radar imaging technique based on CS by random convolution, researches into several different random downsampling strategies. Experiments from simulated data and real data verify the validity of the proposed imaging method, also the influences of SNR and downsampling strategy on imaging performance are analyzed and compared. Finally the problems need further research are pointed out.