Analysis of super-resolution radar imaging based on sparse regularization

For sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then implements several imaging algorithms including FFT and sparse methods. Through comparison, we conclude a good sparse reconstruction algorithm in SAR imaging. Besides, a new strategy of finding a better regularization parameter in sparse reconstruction is implemented. The imaging result of us has a higher resolution and much lower side lobe than the conventional algorithm based on matched filter theory.