Learned 2D-TwISTA for 2-D Sparse ISAR Imaging

Abstract

By unfolding traditional optimization algorithms into the form of neural networks, the unfolding network methods have attracted more and more attention in sparse inverse synthetic aperture radar (ISAR) imaging because of their high reconstruction performance and good interpretability. However, existing unfolding network methods mainly focus on 1-D sparse ISAR imaging and cannot be directly applied to 2-D sparse ISAR data. For this reason, a novel learned 2D-two-step iterative shrinkage/thresholding algorithm (L-2D-TwISTA) is proposed for high-efficiency and high-accuracy 2-D sparse ISAR imaging. Specifically, each stage of L-2D-TwISTA corresponds to an iterative solution step of the developed 2D-TwISTA approach. Moreover, a complex-valued (CV) residual network is designed in L-2D-TwISTA to improve training efficiency and solve the nonlinear problem of proximal mapping of the 2D-TwISTA more effectively. The experimental results of real-measured data confirm that the L-2D-TwISTA can realize high-performance 2-D sparse ISAR imaging.