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In an uncertain ocean environment, the environmental model used by matched field processing (MFP) is different from the real world. As a result of the environmental mismatch, the performance of MFP deceases largely. In order to improve its robustness to the environmental mismatch, a posterior probability constraint matched field processing (MFP-PPC) is proposed. The algorithm derives the posterior probability density (PPD) of the source locations from Bayesian criterion, then the main lobe of AMFP is protected by PPD, so MFP-PPC has not only the merit of high resolution as AMFP, but also the advantage of robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment are used. The results show that MFP-PPC is robust not only to the moored source in the uncertain ocean environment, but also to the moving source. The tracking curve is consistent with the trajectory of the moving source. [ABSTRACT FROM AUTHOR]