中文摘要：

贝叶斯的匹配的地处理的一条途径( MFP )在不明确的海洋 environment.In 被讨论这条途径，无常知识被建模，数组收到的空间、时间的数据是充分 used.Therefore ，为 MFP 的机制是 found.which 很好联合不明确的领域 processing.By 的基于模型、数据驱动的方法理论推导，模拟分析和在海的试验性的数组数据的确认，我们发现那( 1 )贝叶斯的匹配的领域 pr 的基本部件

英文摘要：

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 posteriori 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.