高斯环境和结构干扰下极化雷达自适应检测的设计与性能分析

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing Pub Date : 2025-09-06 DOI:10.1016/j.dsp.2025.105582

Hongzhi Guo, Haoqi Wu, Zhihang Wang, Zishu He, Ziyang Cheng

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引用次数: 0

摘要

研究了结构干扰和高斯噪声下的自适应极化检测。分别在均匀环境（HE）和部分均匀环境（PHE）下，利用一步广义似然比检验（GLRT）和两步广义似然比检验（GLRT）设计了四种检测器。此外，我们还分析了所提出检测器的统计特性，得到了它们的测试统计量的分布。此外，还推导出了所设计的探测器的虚警概率和检测概率的封闭表达式。虚警概率的精确表达式表明，极化检测器在噪声协方差矩阵的作用下是恒虚警比（CFAR）检验。此外，数值实验和理论曲线的仿真结果对所设计的极化探测器的检测性能进行了评价，结果表明所设计的探测器优于竞争对手。

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Design and performance analysis for adaptive detection of polarimetric radar in Gaussian environment and structured interference

This paper addresses the adaptive polarization detection in structured interference and Gaussian noise. Four detectors are designed by utilizing the one-step generalized likelihood ratio test (GLRT) and two-step GLRT both in the homogeneous environment (HE) and partially homogeneous environment (PHE). Besides, we analyse the statistical properties and obtain the distributions of the test statistics of the proposed detectors. Moreover, the closed-form expressions for the probability of false alarm and probability of detection of the designed detectors are derived. The exact expressions of the probability of false alarm demonstrate the polarimetric detectors are constant false alarm ratio (CFAR) tests w.r.t. the noise covariance matrix. Furthermore, the simulation results in numerical experiments and theoretical curves evaluate the detection performance of the proposed polarimetric detectors, and the results show the designed detectors outperform the competitors.

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,