Multiple source localization using a maximum a posteriori Gibbs sampling approach

Multiple source localization in underwater environments is approached within a matched-field processing framework. A maximum a posteriori estimation method is proposed that estimates source location and spectral characteristics of multiple sources via Gibbs sampling. The method facilitates localization of weak sources which are typically masked by the presence of strong interferers. A performance evaluation study based on Monte Carlo simulations shows that the proposed maximum a posteriori estimation approach is superior to simple coherent matched-field interference cancellation. The proposed method is also tested on the estimation of the number of sources present, providing probability distributions in addition to point estimates for the number of sources.