Bayesian model selection applied to spatial signal processing

Inference is applied to the problem of direction of arrival estimation and the determination of the number of sources present in the case of signals impinging on a linear array of sensors. The method is discussed, and the algorithm is found to outperform methods such as AIC and MDL even in the case of very low signal-to-noise ratios and closely spaced sources. The two levels of inference involved in any data analysis problem are introduced and this inference methodology, using the concept of evidence, is then applied to this problem. Simulation results illustrate the power of the method and it is shown that one of the advantages of the present approach is that it is able to handle special cases such as coherent and correlated signals of any functional form, and it does not assume the receivers to be equally spaced