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A novel SAR imaging processing algorithm based on compressive sensing

Synthetic aperture radar (SAR) is a remote sensing system producing images with high resolution. It is not influenced by time and weather. As the increase of resolution and bandwidth, the volume of data augments sharply, bringing great difficulties to store, transmission and processing. Nowadays there is a novel theory named Compressive Sensing (CS) which can recover signal with few samples. Its sample rate is lower than Nyquist rate. In this paper, a novel CS-based SAR imaging algorithm is proposed, in which a random Gaussian measurement matrix, a constructed sparsity matrix and OMP algorithm are employed. The new algorithm can largely reduce the number of samples. The simulation results show the feasibility of the proposed algorithm.