A Novel Compressive Sensing Algorithm for SAR Imaging

摘要

A novel compressive sensing (CS) algorithm for synthetic aperture radar (SAR) imaging is proposed which is called as the two-dimensional double CS algorithm (2-D-DCSA). We first derive the imaging operator for SAR, which is named as the chirp-scaling operator (CSO), from the chirp-scaling algorithm (CSA), then we show its inverse is a linear map, which transforms the SAR image to the received baseband radar signal. We show that the SAR image can be reconstructed simultaneously in the range and azimuth directions from a small number of the raw data. The proposed algorithm can handle large-scale data because both the CSO and its inverse allow fast matrix–vector multiplications. Both the simulated and real data are processed to test the algorithm and the results show that the 2-D-DCSA can be applied to reconstructing the SAR images effectively with much less data than regularly required.