High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net

Remote Sensing

Feng Zhou¹  Xiaoyong Li¹  Xueru Bai²  Yujie Zhang² 

[1] Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, China;National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China;

关键词: inverse synthetic aperture radar (ISAR) imaging;    sparse signal reconstruction;    plug-and-play;    unrolling;    plug-and-play 2D ADMM-Net (PAN);   

DOI  :  10.3390/rs14040901

来源: DOAJ

【 摘 要 】

We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signal-to-noise ratios (SNR) and incomplete data scenarios. First, a sparse observation model of 2D ISAR imaging is established, and a 2D ADMM algorithm is presented. On this basis, using the plug and play (PnP) technique, PnP 2D ADMM is proposed, by combining the 2D ADMM algorithm and the deep denoising network DnCNN. Then, we unroll and generalize the PnP 2D ADMM to the PAN architecture, in which all adjustable parameters in the reconstruction layers, denoising layers, and multiplier update layers are learned by end-to-end training through back-propagation. Experimental results showed that the PAN with a single parameter set can achieve noise-robust ISAR imaging with superior reconstruction performance on incomplete simulated and measured data under different SNRs.

【 授权许可】

Unknown