Sparse aperture ISAR imaging algorithm based on adaptive filtering framework

Abstract

Under sparse aperture conditions, some problems arise with inverse synthetic aperture radar (ISAR) imaging such as low-azimuth resolution and susceptibility to noise. To solve them, the sparseness of scattering points is used to transform the imaging problem into the sparse signal reconstruction problem, and a sparse aperture ISAR imaging algorithm based on adaptive filtering framework is proposed, which is named smoothed L0 norm-Newton's method least mean square (LMS) algorithm. Firstly, the L0 norm LMS algorithm is taken as the reconstruction method for its advantages of simple structure and high-reconstruction accuracy. Then, the smoothed L0-norm method is extended to the complex domain of radar signal processing to increase the accuracy and maintain a good robustness. Finally, in order to speed up the convergence, Newton's method is introduced to the LMS algorithm. Simulation results show that the reconstruction image of the proposed method has higher resolution and better anti-noise performance than those of other reconstruction algorithms.