An approach of Bayesian Matched Field Processing (MFP) was discussed in the uncertain ocean environment.In this approach,uncertainty knowledge is modeled and spatial and temporal data received by the array are fully used.Therefore,a mechanism for MFP is found,which well combines model-based and data-driven methods of uncertain field processing. By theoretical derivation,simulation analysis and the validation of the experimental array data at sea,we find that (1) the basic components of Bayesian matched field processors are the cor- responding sets of Bartlett matched field processor,MVDR (minimum variance distortionless response) matched field processor,etc.;(2) Bayesian MVDR/Bartlett MFP are the weighted sum of the MVDR/Bartlett MFP,where the weighted coefficients are the values of the a poste- r~or~ probability;(3) with the uncertain ocean environment,Bayesian MFP can more correctly locate the source than MVDR MFP or Bartlett MFP;(4) Bayesian MFP can better suppress sidelobes of the ambiguity surfaces. An approach of Bayesian Matched Field Processing (MFP) was discussed in the uncertain ocean environment. In this approach, uncertainty knowledge is modeled and spatial and temporal data received by the array are fully used. Before, a mechanism for MFP is found, which well based on theoretical derivation, simulation analysis and the validation of the experimental array data at sea, we find that (1) the basic components of Bayesian matched field processors are the cor responding sets of Bartlett matched field processor, MVDR (minimum variance distortionless response) matched field processor, etc .; (2) Bayesian MVDR / Bartlett MFP are the weighted sum of the MVDR / Bartlett MFP, where the weighted coefficients are the values ​​of the a (3) with the uncertain ocean environment, Bayesian MFP can more correctly locate the source than MVDR MFP or Bartlett MFP; (4) Bayesian MFP can better suppress sidelobes of the ambiguity surfaces.