Analysis of Sparsity Based Joint Sar Image Reconstruction and Autofocus Techniques

Abstract

Synthetic Aperture Radar (SAR) has significance in many remote sensing applications. One of the main problems with SAR is the platform motion that causes defocusing in the reconstructed SAR image. To mitigate this problem, for particularly on imaging of fields that admit a sparse representation, various sparsity based techniques that either apply optimization procedures or greedy iterative solutions have been proposed in the literature. Although these techniques have been mainly compared with classical phase gradient autofocus (PGA) algorithm, they have not been analyzed and compared with each other. In this paper several of the recent sparsity based SAR phase correction techniques are compared using metrics such as mean square error (MSE), entropy, target to background ratio (TBR) in terms of undersampling ratio, signal to noise ratio (SNR). In addition to comparisons, a cross validation based stopping criterion is introduced with an OMP procedure to free the algorithm from user defined parameters. The techniques are tested on simulated data for detailed comparisons. Real data results of tested techniques are also provided. Our initial results show that all compared sparsity based techniques provide better performance compared to PGA with varying relative performances.