Application of the Reversible Jump MCMC Method to Array Signal Processing
This paper presents the application of the Reversible Jump Markov Chain Monte Carlo (RJMCMC) Method to the problem of joint detection and estimation of sources impinging on a single array of sensors in Gaussian noise. A new Bayesian array signal model structure based on signal reconstruction is proposed, that allows us to define a posterior distribution on the parameter space, which is applicable to both wideband and narrowband signal. In simulation, it appears that the performance of detection based on posterior model probabilities outperforms conventional detection schemes, using significantly fewer observations and that only real arithmetic is required.
Signal Reconstruction Bayesian Estimation Markov Chain Monte Carlo Source Number Detection Direction of Arrival
JIN Meina ZHAO Yongjun GE Jiangwei
Zhengzhou Information Science and Technology Institute, Zhengzhou, P.R.China, 450002
国际会议
2008年国际应用统计学术研讨会(2008 International Institute of Applied Statistics Studies)
烟台
英文
1-5
2008-08-14（万方平台首次上网日期，不代表论文的发表时间）