Research on bayesian radar adaptive detection

被引：0

作者：

Zhou, Yu ^{[1
]}

Zhang, Linrang ^{[1
]}

Liu, Nan ^{[1
]}

机构：

[1] National Key Lab. of Radar Signal Processing, Xidian Univ.

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2012年 / 39卷 / 01期

关键词：

Bayesian approach; Heterogeneous environment; Rao test; Wald test;

D O I：

10.3969/j.issn.1001-2400.2012.01.006

中图分类号：

学科分类号：

摘要：

We consider the problem of detecting a signal of interest in the presence of Gaussian noise with the unknown covariance matrix (CM). The traditional approach relies on modeling CM as a deterministic parameter, and its maximum likelihood (ML) estimation is derived when designing the adaptive detector. The ignorance of prior distribution incurs performance loss when there are only a few training data. In this paper, a different approach is proposed which models CM as a random parameter with inverse Wishart distribution. Under this assumption, the maximum a-posteriori (MAP) estimation of CM is derived. The MAP estimate is in turn used to yield the Bayesian version of the Rao and Wald detector. And the importance of the a priori knowledge can be tuned through the scalar variable. The devised detectors remarkably outperform the non-Bayesian Rao and Wald test in the presence of strongly heterogeneous scenarios (where a very small number of training data are available).

引用

页码：28 / 33

页数：5

共 12 条

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