不确实海洋环境下的贝叶斯匹配场处理

A Bayesian approach to matched field processing in uncertain ocean environments

讨论了一种不确实海洋环境下贝叶斯匹配场处理方法。该方法在不确实环境下匹配场处理时,对不确实知识进行建模,同时,充分利用阵接收的空时数据,找到了匹配场处理时一种结合模基和数据驱动的机制。经理论推导、仿真分析以及实验海试数据验证,结果表明:(1)最小方差无偏响应(MVDR)、Bartlett等匹配场处理器是贝叶斯处理器的基本单元;(2)贝叶斯匹配场处理是对应的MVDR匹配场处理、Bartlett匹配场处理器等匹配场处理方法的后验概率加权和;(3)不确实海洋环境下,贝叶斯方法比对应的MVDR处理器和Bartlett处理器能更准确地对目标进行定位;(4)贝叶斯方法能较有效地抑制旁瓣。

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.