Real Aperture Radar Angular Super-Resolution Imaging Using Modified Smoothed L0 Norm with a Regularization Strategy

Shuifeng Yang; Yong Zhao; Xingyu Tuo; Deqing Mao; Yin Zhang; Jianyu Yang

doi:10.3390/rs16010012

Remote Sensing (Dec 2023)

Real Aperture Radar Angular Super-Resolution Imaging Using Modified Smoothed L0 Norm with a Regularization Strategy

Shuifeng Yang,
Yong Zhao,
Xingyu Tuo,
Deqing Mao,
Yin Zhang,
Jianyu Yang

Affiliations

Shuifeng Yang: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yong Zhao: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Xingyu Tuo: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Deqing Mao: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yin Zhang: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Jianyu Yang: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

DOI: https://doi.org/10.3390/rs16010012
Journal volume & issue: Vol. 16, no. 1
p. 12

Abstract

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Restricted by the ill-posed antenna measurement matrix, the conventional smoothed L0 norm algorithm (SL0) fails to enable direct real aperture radar angular super-resolution imaging. This paper proposes a modified smoothed L0 norm (MSL0) algorithm to address this issue. First, as the pseudo-inverse of the ill-posed antenna measurement matrix is required to set the initial values and calculate the gradient projection, a regularization strategy is employed to relax the ill-posedness. Based on the regularization strategy, the proposed MSL0 algorithm can avoid noise amplification when faced with the ill-posed antenna measurement matrix of real aperture radar. Additionally, to prevent local minima problems, we introduce a hard thresholding operator, based on which the proposed MSL0 algorithm can accurately reconstruct sparse targets. Simulations and experimental results verify the performance of the proposed MSL0 algorithm.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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