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Physical model-driven deep networks for through-the-wall radar imaging

Published online by Cambridge University Press:  03 February 2022

Yuhao Wang ,
Yue Zhang Open the ORCID record for Yue Zhang [Opens in a new window] ,
Mingcheng Xiao ,
Huilin Zhou ,
Qiegen Liu  and
Jianfei Gao
Yuhao Wang*
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Yue Zhang
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Mingcheng Xiao
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Huilin Zhou
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Qiegen Liu
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Jianfei Gao
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
*
Author for correspondence: Yuhao Wang, E-mail: wangyuhao@ncu.edu.cn
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Abstract

In order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is introduced to replace the regularization term in the traditional iterative CS-based method to capture the redundancy of the radar echo signal. Moreover, the physical model of the radar signal is used in the data consistency layer to encourage consistency with the measurements. Second, the iterative CS optimization is unrolled to yield a deep learning network, where the weight, regularization parameter, and the other parameters are learnable. A quantity of training data enables the network to extract high-dimensional characteristics of the radar echo signal to reconstruct the spatial target image. Simulation results demonstrated that the proposed method can achieve accurate target image reconstruction and was superior to the traditional CS method, in terms of mean squared error and the target texture details.

Keywords

Compressed sensingdeep learningimage reconstructionTWRIunrolling network
Type
Radar
Information
International Journal of Microwave and Wireless Technologies , Volume 15 , Special Issue 1: EuCAP 2021 Special Issue , February 2023 , pp. 82 - 89
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press in association with the European Microwave Association

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