The unfolding network, which combines traditional compressive sensing methods and deep learning, has been widely used in sparse aperture (SA) inverse synthetic aperture radar (ISAR) imaging because of its good interpretability, high reconstruction accuracy, and computational efficiency. However, most unfolding networks view SA ISAR imaging under different sampling matrices as diverse problems, resulting in poor network transferability when the sampling matrix changes. In this article, a complex-valued residual alternating direction method of multipliers-Net (CV-Res-ADMM-Net) is designed to address this issue. Based on the radar signal's complex-valued (CV) characteristics, we designed a residual learning-based CV convolutional neural network that includes three modules: feature extraction, nonlinear mapping, and feature accumulation to implement the nonlinear operation to enhance the network's ISAR image feature extraction capability. The residual learning technique can accelerate the training process and smooth network information transmission. Moreover, a multisampling matrix generation approach is developed, which can improve the training diversity and maintain more structural information on ISAR images for more satisfactory reconstruction. Simulated and real-measured experiments verify that CV-Res-ADMM-Net has good transferability when the sampling matrix changes. Experimental results show the proposal can achieve fast SA ISAR imaging of maneuvering targets.
CV-Res-ADMM-Net for Sparse Aperture ISAR Imaging
IEEE Transactions on Aerospace and Electronic Systems ; 61 , 4 ; 8291-8305
01.08.2025
4356106 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Resolution enhanced sparse aperture imaging
IEEE | 2006
|Origins of sparse aperture imaging
IEEE | 2001
|