Reversible Jump MCMC for Joint Dectection and Estimation of Directions of Arrival in Coloured Noise

IEEE Transactions on Signal Processing,  Special Issue on Monte Carlo Methods, Vol. 50, No. 2, Feb. 2002, pp. 231-240
J.-R. Larocque and J.P. Reilly

Abstract   This paper presents a novel Bayesian solution to the difficult problem of joint detection and estimation of sources impinging on a single array of sensors in spatially coloured noise with arbitrary covariance structure. Robustness to the noise covariance structure is achieved by integrating out the unknown covariance matrix in an appropriate posterior distribution. The proposed procedure uses the Reversible Jump Markov Chain Monte Carlo method to extract the desired model order and direction of arrival parameters. We show that the determination of model order is consistent provided a particular hyperparameter is within a specified range. Simulation results support the effectiveness of the method.