Improved Adaptive Parameter Estimation For Sparse Sar Imaging Based On Complex Image And Azimuth-Range Decouple

Sparse signal processing theory has been applied to SAR imaging. The estimation of sparsity is crucial for sparse SAR imaging. But the true value of sparsity is unknown. Adaptive parameter estimation for sparse SAR imaging can be achieved by the automatic regularization parameter estimating methods. However, these methods are deduced based on measurement matrix, which will cause huge computational and memory costs. Also, the adaptive estimated sparsity is often greater than the true value due to the noise and sidelobes. In this paper, we propose an improved adaptive parameter estimation method for sparse SAR imaging. The complex-image-based sparse SAR imaging is adopted to pre-estimate the parameter. Then, the azimuth-range decouple operators are introduced into the parameter estimation method. Simulation and real data experimental results show the effectiveness of the proposed method.