Joint 2-D Sparse ISAR Imaging and Autofocusing by Using 2-D-IADIANet
Mingjiu Lv, Wenfeng Chen, Jun Yang, Dangwei Wang, Xia Wu, Xiaoyan Ma
Abstract
Compressive sensing (CS)-based methods have been widely used for sparse inverse synthetic aperture radar (ISAR) imaging. However, many CS-based methods are sensitive to the selection of model parameters, and the residual phase error of the echo also causes trouble for imaging and autofocusing. To address these problems, a novel deep learning approach, named as 2-D-IADIANet, is proposed to achieve 2-D sparse ISAR imaging with 2-D phase error estimation in this article. First, a 2-D ISAR sparse echo model with 2-D phase error into account is established, and a 2-D alternating direction method of multipliers (2-D-ADMM) frame-work-based method, dubbed as 2-D-IADIA, is presented to solve this compound reconstruction problem. Second, a 2-D-IADIA is further unfolded and mapped into a deep network form by integrating with a 2-D phase error compensation network. Moreover, all adjustable parameters can be learned adaptively by training the network through a back propagation algorithm in a complex domain directly. Finally, experimental results verify that the well-learned 2-D-IADIANet, which is only trained by a small amount of simulation samples, can also be generalized to measured data application. Especially, owing to the good performance of the network, the proposal has a superior reconstruction performance than 2-D-IADIA under the low 2-D sample rate and/or signal-to-noise ratio scenarios.