Robust Conditional Probability Constraint Matched Field Processing

In order to improve the robustness of Adaptive Matched Field Processing(AMFP), a Conditional Probability Constraint Matched Field Processing (MFP-CPC) is proposed. The algorithm derives the posterior probability density of the source locations from Bayesian Criterion, then the main lobe of AMFP is protected and the side lobe is restricted by the posterior probability density, so MFP-CPC not only has the merit of high resolution as AMFP, but also improves the robustness. To evaluate the proposed algorithm, the canonical test case of the Naval Research Laboratory (NRL) is used. The results show that MFP-CPC is better than Bartlett and MVDR. Its robustness is like Bartlett, and its main lobe is the same as that of MVDR, meanwhile its side lobe is lower about 6 to 8dB than the latter.