Analysis of the effect of sparsity on the performance of SAR imaging based on CS theory

The sample rate of SAR imaging algorithm based on compressive sensing theory is well below the traditional imaging method, and can also accurately reconstruct the original signal. The SAR imaging based on compressive sensing theory has become a hot issue in this area. It is generally considered that the imaging result based on this method has no sidelobe. In this paper, by changing the sparsity of the echo signal, the influence of the sparsity parameter on the sidelobe's performance of the imaging result is quantitatively analyzed. The result shows that when the sparsity parameter is small, the sidelobe of imaging result is also small, and even doesn't exist, however, with the parameter value increasing, the sidelobe continuously enhanced, eventually closing to the theoretical value of the result based on classic pulse compression. Therefore, for the targets outside the scene of the coefficient lattice, the imaging method based on compressive sensing theory can not improve the sidelobe's performance of the imaging result.