Accurate SAR Image Recovery From RFI Contaminated Raw Data by Using Image Domain Mixed Regularizations

Abstract

Radio frequency interference (RFI) suppression is a hot topic in synthetic aperture radar (SAR) imaging. Mathematically, the RFI suppression problem can be considered as an underdetermined signal separation problem to extract the signal of interest (SOI) from the RFI contaminated raw data. The regularization-based method can exploit both the prior knowledge of RFI and SOI and, therefore, has the advantage of solving the underdetermined problem and preserving the information of SOI. Current regularization methods make use of the RFI prior well by exploiting low-rank representation (LRR) or sparse representation (SR), but the prior knowledge of SOI has not been sufficiently studied and used. In some literature, the sparsity of the raw data or range profile was exploited to formulate the regularization term, which we found to be inadequate in describing the SOI property. In this article, we explore the features of SAR images and propose an RFI suppression model with a combination of multiple image domain regularizations to preserve different types of targets. An efficient solution to the optimization problem is proposed based on the alternating direction multiplier method (ADMM). The proposed method can accurately recover both the sparse strong targets, and the nonsparse regions in the illuminated area and its performance is validated by measured data.