Rao Test With Improved Robustness for Range-Spread Target Detection

被引：0
作者：
Sun, Shengyin [1 ]
Liu, Jun [1 ]
Liu, Weijian [2 ]
机构：
[1] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China
[2] Wuhan Elect Informat Inst, Wuhan 430019, Peoples R China
来源：
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) | 2021年
基金：
中国国家自然科学基金;
关键词：
Adaptive detection; distributed targets; constant false alarm rate; steering vector mismatch; Rao test;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
This paper deals with the problem of detecting range-spread targets in Gaussian noise with unknown covariance matrix. We model the received signal under the signal-plus-noise hypothesis as the sum of noise, useful target echoes and fictitious signals, which makes the signal-plus-noise hypothesis more plausible in the mismatched case. An adaptive detector is designed according to the Rao test. We prove the proposed Rao test exhibits constant false alarm rate property against the covariance matrix. Numerical examples show that the proposed Rao outperforms its counterparts in the mismatched case.
引用
收藏
页码：1916 / 1920
页数：5
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