不确定环境下后验概率约束的匹配场处理

Posterior Probability Constraint Matched Field Processing with Environmental Uncertainty

不确定海洋环境下,匹配场处理(MFP)所使用的环境模型与真实环境总会存在一定的差异,即存在环境参数失配,此时匹配场处理的性能严重下降。为了提高匹配场处理的稳健性,提出了一种后验概率约束的匹配场处理器(MFP-PPC)。利用贝叶斯准则推导出了位置参数的后验概率密度(PPD)估计,以此为约束为自适应匹配场处理器(AMFP)提供主瓣保护,使得算法具有AMFP高分辨特点的同时稳健性也得到改善。为了验证算法的性能,使用不确定浅海环境下的仿真数据和海试数据进行了处理分析。分析结果表明:不确定环境下,MFP-PPC不仅能够稳健地定位固定声源,而且能够稳健地定位运动声源,且跟踪曲线和声源移动轨迹吻合较好。

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 prob- ability 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 advan- tage of robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shal- low 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.