Matched field processing under posterior probability constraints

When under uncertain ocean environment, the environmental model of matched field processing (MFP) is different from the real world. The performance of MFP decreases largely because of the environmental mismatch. In order to improve its robustness to the environmental mismatch, a MFP with posterior probability constraints (MFP-PPC) is proposed. The algorithm derives the posterior probability density function (PDF) of the source locations from Bayesian Criterion, then the main lobe of AMFP is protected by posterior PDF, so the MFP-PPC has not only the merit of high resolution as AMFP, but also the advantage of robustness. We use experimental data to evaluate the algorithm, the results shown that in the uncertain ocean environment the MFP-PPC is robust not only to the moored source, but also to the moving source. Meanwhile, the localization and tracking is consistent with the trajectory of the moving source.