复合高斯噪声中知识辅助的贝叶斯Rao检测方法

doi:10.3969/j.issn.1001-2400.2013.06.008

Abstract

Abstract:

An asymptotically likelihood covariance matrix is usually used to detect a target in compound Gaussian noise. However, it is influenced greatly by the training sample support, and it ignores the prior distribution of the covariance matrix. For this problem, this paper proposes the knowledge-aided Bayesian Rao detection. The covariance matrix in compound Gaussian noise is modeled as a random matrix, the prior distribution of which satisfies the complex inverse Wishart distribution. With prior distribution, the maximum a-posterior estimation of the covariance matrix is derived. Then, Rao detection is obtained based on the maximum a-posterior estimation. Finally, the performance of the knowledge-aided Bayesian Rao detection approach is evaluated by Monte Carlo simulation. The simulation results show that the detection performance of the proposed approach outperforms the traditional detection approaches when the number of training samples is small in a complex Gaussian noise scenario.

Key words: compound Gaussian noise, Rao detection, covariance matrix, prior distribution

CLC Number:

TN957.51

GAO Yongchan;LIAO Guisheng;ZHU Shengqi. Knowledge-aided Bayesian Rao detection approach in compound Gaussian noise[J].J4, 2013, 40(6): 46-51+173.

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Knowledge-aided Bayesian Rao detection approach in compound Gaussian noise

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