A Bayesian approach to matched field processing in uncertain ocean environments

An approach of Bayesian Matched Field Processing (MFP) is 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.By theoritical 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 corresponding sets of MVDR matched field processor,Bartlett 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 posteriori probability; (3) with the uncertain ocean environment,Bayesian MFP can more correctly locate the position of the source than MVDR MFP and Bartlett MFP; (4) Bayesian MFP can better suppress the ambiguities.