贝叶斯雷达自适应检测算法研究

doi:10.3969/j.issn.1001-2400.2012.01.006

J4 ›› 2012, Vol. 39 ›› Issue (1): 28-33.doi: 10.3969/j.issn.1001-2400.2012.01.006

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Research on Bayesian radar adaptive detection

ZHOU Yu;ZHANG Linrang;LIU Nan

(National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an 710071， China)

Received:2010-11-15 Online:2012-02-20 Published:2012-04-06
Contact: ZHOU Yu E-mail:zhouyu@mail.xidian.edu.cn

Abstract

Abstract:

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).

Key words: Bayesian approach, Rao test, Wald test, heterogeneous environment

CLC Number:

TN957

ZHOU Yu;ZHANG Linrang;LIU Nan. Research on Bayesian radar adaptive detection[J].J4, 2012, 39(1): 28-33.

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[2]	ZHOU Yu;ZHANG Lin-rang;LIU Nan;LIU Xin. Knowledge-aided space-time adaptive detection method [J]. J4, 2010, 37(3): 454-458+546.

Research on Bayesian radar adaptive detection

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