Bayesian detection for distributed target with limited training data

被引：0

作者：

Zhou, Zhe ^{[1
,2
]}

Wu, Yuntao ^{[1
,2
]}

Liu, Weijian ^{[3
]}

Liu, Jun ^{[4
]}

Gong, Pengcheng ^{[1
,2
]}

机构：

[1] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan 430205, Peoples R China

[2] Wuhan Inst Technol, Hubei Key Lab Intelligent Robot, Wuhan 430205, Peoples R China

[3] Wuhan Elect Informat Inst, Wuhan 430019, Peoples R China

[4] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China

来源：

DIGITAL SIGNAL PROCESSING | 2024年 / 149卷

基金：

中国国家自然科学基金;

关键词：

Adaptive detection; Bayesian; Low sample support; Multichannel signal detection; ADAPTIVE DETECTION; MIMO RADAR; SUBSPACE DETECTION; RAO TEST; RANGE; GLRT; INTERFERENCE;

D O I：

10.1016/j.dsp.2024.104475

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The issue of subspace-based distributed target detection with limited training data is addressed in this study. We use the Bayesian method to tackle the issue by assuming that the covariance matrix follows an inverse Wishart distribution. According to the generalized likelihood ratio test, Rao test, and Wald test, three Bayesian detectors are designed. Real data and simulations both attest to the usefulness of the proposed detectors.

引用

页数：6

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