杂乱
计算机科学
恒虚警率
正规化(语言学)
算法
合成孔径雷达
人工智能
压缩传感
雷达
模式识别(心理学)
电信
作者
Pucheng Li,Zegang Ding,Tianyi Zhang,Yangkai Wei,Yongpeng Gao
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-15
被引量:3
标识
DOI:10.1109/tgrs.2023.3251732
摘要
Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried out in ℓ 1 -norm regularization and considers that the SAR image contains only targets and noise, which ignores the clutter and seriously degrades classical algorithms. To address this problem, we propose an integrated detection and imaging algorithm for radar sparse targets with constant false alarm rate (CFAR) regularization by alternative direction method of multipliers (ADMM), called CFAR-ADMM, and we further introduce total variation (TV) regularization and propose the more robust CFAR-TV-ADMM. First, a more complete echo signal model which considers targets, the clutter, and the noise simultaneously is established. Then, inspired by the CFAR detection, a novel regularization with sparse target awareness is proposed. The proposed regularization can obtain the statistical characteristics of clutter and noise region by region, and distinguish whether the current cell contains the target effectively and accurately. Benefiting from this novel regularization, CFAR-ADMM and TV-CFAR-ADMM can not only realize the sparse imaging but also detect sparse targets simultaneously, which can reduce the propagation error caused by cascading processing and improve the solution accuracy. Finally, the proposed algorithm is verified by simulation data results, phase transition analysis, and real data experiments.
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