摘要
为了提高自适应匹配场处理（AMFP）的稳健性，该文提出一种条件概率约束的自适应匹配场处理算法（MFP-CPC）。该算法利用贝叶斯准则推导出了位置参数的后验概率密度估计，以此作为权系数给自适应匹配场处理器（AMFP）提供主瓣保护和旁瓣压缩性能，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NRL的典型测试数据进行了仿真分析。结果表明：MFP-CPC具有优于Bartlett和最小方差无失真响应（MVDR）的定位性能，其稳健性和Bartlett类似，主瓣宽度和MVDR相同且旁瓣比MVDR低约6~8 dB。
In order to improve the robustness of Adaptive Matched Field Processing （AMFP）, a Conditional Probability Constraint Matched Field Processing （MFP-CPC） is proposed. The algorithm derives the posterior probability density of the source locations from Bayesian Criterion, then the main lobe of AMFP is protected and the side lobe is restricted by the posterior probability density, so MFP-CPC not only has the merit of high resolution as AMFP, but also improves the robustness. To evaluate the proposed algorithm, the canonical test case of the Naval Research Laboratory （NRL） is used. The results show that MFP-CPC is better than Bartlett and Minimum Variance Distoritionless Response （MVDR）. Its robustness is like Bartlett, and its main lobe is the same as that of MVDR, meanwhile its side lobe is lower about 6 to 8 dB than the latter.
出处
《电子与信息学报》
EI
CSCD
北大核心
2014年第10期2425-2430,共6页
Journal of Electronics & Information Technology
基金
国家自然科学基金(51209173)资助课题
关键词
水声信号处理
自适应匹配场处理
后验概率密度
稳健性
Underwater signal processing
Adaptive Matched Field Processing （AMFP）
Posterior probabilitydensity
Robustness
