GLRT detector based on knowledge aided covariance estimation in compound Gaussian environment

被引：10

作者：

Shang, Zheran ^{[1
]}

Li, Xiang ^{[1
]}

Liu, Yongxiang ^{[1
]}

Wang, Yongliang ^{[2
]}

Liu, Weijian ^{[2
]}

机构：

[1] Natl Univ Def Technol, Sch Elect Sci, Changsha 410073, Hunan, Peoples R China

[2] Wuhan Elect Informat Inst, Wuhan 430019, Hubei, Peoples R China

来源：

SIGNAL PROCESSING | 2019年 / 155卷

基金：

中国国家自然科学基金;

关键词：

Knowledge-based; Adaptive detection; Compound Gaussian clutter; ADAPTIVE DETECTION; STATISTICAL-ANALYSIS; MATRIX ESTIMATION; TARGET DETECTION; CLUTTER; RADAR; STAP;

D O I：

10.1016/j.sigpro.2018.10.008

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：

In order to alleviate the effect of the limited secondary data in the non-Gaussian clutter, a knowledge aided adaptive detector is proposed. The covariance matrix estimation is modeled as a general linear combination of prior covariance matrix and sample covariance matrix. Within this consideration, we obtain an adaptive detector based on the generalized likelihood ratio test. Experimental results on simulation and real data demonstrate that the proposed detector achieves better performance than the existing one-step GLRT (1S-GLRT) detectors when the secondary data are insufficient. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：377 / 383

页数：7

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