Wideband Array Signal Processing Using MCMC Methods

This paper proposes a novel wideband structure for array signal processing. A new wideband model is formed where the observations are linear functions of the source amplitudes, but nonlinear in the direction of arrival (DOA) parameters. The method lends itself well to a Bayesian approach for jointly estimating the model order and the DOAs through a reversible jump Markov chain Monte Carlo (MCMC) procedure. The source amplitudes are estimated through a maximum a posteriori (MAP) procedure. The DOA estimation performance of the proposed method is compared with the theoretical Cramér-Rao lower bound (CRLB) for this problem. Simulation results demonstrate the effectiveness and robustness of the method.