NBI Suppression Method for SAR Based on Sparse Segmentation Search

In remote sensing research, narrowband interference (NBI) suppression in synthetic aperture radar (SAR) is an urgent problem. Recently, many methods on NBI suppression are proposed via sparse recovery. In these methods, the optimal regularization constants are always hard to choose. Moreover, the number of NBI signals, equaling to the sparsity of the sparse vector, may change at different pulses, and the suppression performance might be reduced since the regularization constants that control the sparsity of the sparse vector are fixed. In this letter, aiming at these problems, an NBI suppression method for SAR based on sparse segmentation search (SSS) is proposed. First, we build a nonconvex optimization model without the regularization constant. Then, the adaptive linear enhancer (ALE) is used to convert the nonconvex model to a convex one. Finally, we solve this convex model and suppress NBI signals. The real-world SAR data experiments illustrate the effectiveness of the proposed method.