Compressive near-field millimeter wave imaging algorithm based on gini index and total variation mixed regularization

Q1 Engineering

Journal of Electronic Science and Technology Pub Date : 2023-03-01 DOI:10.1016/j.jnlest.2023.100191

Jue Lyu , Dong-Jie Bi , Bo Liu , Guo Yi , Xue-Peng Zheng , Xi-Feng Li , Li-Biao Peng , Yong-Le Xie , Yi-Ming Zhang , Ying-Li Bai

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引用次数: 1

Abstract

A compressive near-field millimeter wave (MMW) imaging algorithm is proposed. From the compressed sensing (CS) theory, the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data. The Gini index (GI) has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates, and Babies. By combining the total variation (TV) operator, the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed. In addition, the corresponding algorithm based on a primal-dual framework is also proposed. Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l₁-TV mixed regularization algorithm.

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基于基尼指数和总变分混合正则化的压缩毫米波近场成像算法

提出了一种压缩近场毫米波成像算法。从压缩感知(CS)理论出发，可以考虑压缩近场毫米波成像过程，从欠采样稀疏数据中重构图像。基尼指数(GI)被认为是唯一一个具有罗宾汉、缩放、涨潮、克隆、比尔盖茨和婴儿等所有稀疏性属性的稀疏性度量。结合总变差算子，提出了GI-TV混合正则化引入的压缩近场毫米波成像模型。此外，还提出了基于原对偶框架的相应算法。实验结果表明，与目前广泛使用的l1-TV混合正则化算法相比，本文提出的GI-TV混合正则化算法具有更好的收敛性和稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering

CiteScore

4.30

自引率

0.00%

发文量

1362

审稿时长

99 days

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