Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
Statistics and Computing
Fast algorithm of the ML estimator for passive synthetic arrays
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Bayesian spectral estimation applied to echo signals from nonlinear ultrasound scatterers
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
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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 colored 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 (MCMC) 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