基于贝叶斯算法的声源运动参数估计方法
详细信息 查看官网全文
- 英文论文题名：Moving source parameter estimation based on Bayesian algorithm
- 论文作者：李倩倩 ; 阳凡林 ; 张凯 ; 孙梅
- 英文论文作者：LI Qian-qian ; YANG Fan-lin ; ZHANG Kai ; SUN Mei ; College of Geomrtics ; Shandong University of Science and Technology ; State Key Laboratory of Acoustics ; Institute of Acoustics ; Chinese Academy of Sciences ; School of Physics and Electronic Engineering ; Taishan University
- 年：2016
- 作者机构：山东科技大学测绘科学与工程学院海洋测绘系;中国科学院声学研究所声场声信息国家重点实验室;泰山学院物理与电子工程学院;
- 论文关键词：不确定海洋环境 ; 匹配场定位 ; 贝叶斯理论 ; 运动声源
- 英文论文关键词：uncertain ocean environment ; matched-field localization ; Bayesian theory ; moving source
- 会议召开时间：2016-10-28
- 会议录名称：2016年全国声学学术会议论文集
- 语种：中文
- 分类号：O427
- 学会代码：OGSM
- 会议名称：2016年全国声学学术会议
- 会议地点：中国湖北武汉
- 主办单位：中国声学学会
- 学会名称：中国声学学会
- 页数：4
- 文件大小：431k
- 原文格式：D
- 会议级别：全国
摘要
环境参数失配导致定位性能大幅度下降是匹配场定位所面临的难题之一.应用贝叶斯理论对环境聚焦,是当前解决该难题的研究热点.然而,运动声源的位置时变性限制了观测时间长度和观测信息量.当航速较快或是环境信息的不确定性较大时,环境聚焦方法的效果迅速变差.借鉴卡尔曼滤波处理非平稳过程的参数估计思想,本文将多个时刻的接收信号同时反演,引入能够描述声源位置随时间变化规律的时不变参数,同时降低待估参数的维数.将当前估计结果作为下一次反演的先验信息,实现运动声源的连续定位.该方法在相同的环境不确定条件下,大幅度增加了观测时间和观测信息量,可以较好地改善环境聚焦方法的定位效果.
Environmental uncertainty often represents the limiting factor in matched-field localization. Within a Bayesian framework, environmental focalization has been widely applied to solve the augmented localization problem. However, the position of the moving source changes with time, which limits the observation interval and the number of acoustic signals. When the source moves fast or the environment has great uncertainty, environmental focalization gets worse. Using the thought of Kalman filter processing parameter estimation of non-stationary, acoustic signals from a series of observations are treated in a simultaneous inversion. This article introduces time-unvarying parameters to describe the source position, and the source positions are inverted indirectly by the time-unvarying parameters, which reduces the estimated parameter dimension effectively. At the same time, the current estimated results are treated as the next inversion as a priori information. It could realize continuous localization of the moving source. The Bayesian tracking algorithm greatly increases the observation interval and the number of acoustic signals, and enhances the localization accuracy in an uncertain water environment.
Environmental uncertainty often represents the limiting factor in matched-field localization. Within a Bayesian framework, environmental focalization has been widely applied to solve the augmented localization problem. However, the position of the moving source changes with time, which limits the observation interval and the number of acoustic signals. When the source moves fast or the environment has great uncertainty, environmental focalization gets worse. Using the thought of Kalman filter processing parameter estimation of non-stationary, acoustic signals from a series of observations are treated in a simultaneous inversion. This article introduces time-unvarying parameters to describe the source position, and the source positions are inverted indirectly by the time-unvarying parameters, which reduces the estimated parameter dimension effectively. At the same time, the current estimated results are treated as the next inversion as a priori information. It could realize continuous localization of the moving source. The Bayesian tracking algorithm greatly increases the observation interval and the number of acoustic signals, and enhances the localization accuracy in an uncertain water environment.
引文
[1]Gerstoft P and Mecklenbrauker C F.Ocean acoustic inversion with estimation of a posteriori probability distributions.J.Acoust.Soc.Am.,1998;104(2):808-819
[2]Stan E Dosso,Michael J Wilmut,Bayesian focalization:Quantifying source localization with environmental uncertainty,J.Acoust.Soc.Am.,2007,121(5):2567-2574
[3]S L Tantum and L W Nolte.Tracking and localizing a moving source in an uncertain shallow water environment.J.Acoust.Soc.Am.,1998,103(1):362-373
[4]Stan E Dosso and Michael J wilmut.Comparison of focalization and marginalization for Bayesian tracking in an uncertain ocean environment.J.Acoust.Soc.Am.,2009,125(2):717-722
[5]Stan E Dosso and Michael J wilmut,Bayesian source tracking via focalization and marginalization in an uncertain Mediterranean Sea environment,J.Acoust.Soc.Am.,2010,128(1):66-74
[6]李整林,郡锦,李风华等.由简正波群延时及幅度反演海底参数.声学学报,2002;27(6):487-491LI Zhenglin,YAN Jin,LI Fenghua.Inversion for the sea bottom acoustic parameters by using the group time delays and amplitude of normal mode,Acta Acustica,2002,27(6):487-491
[2]Stan E Dosso,Michael J Wilmut,Bayesian focalization:Quantifying source localization with environmental uncertainty,J.Acoust.Soc.Am.,2007,121(5):2567-2574
[3]S L Tantum and L W Nolte.Tracking and localizing a moving source in an uncertain shallow water environment.J.Acoust.Soc.Am.,1998,103(1):362-373
[4]Stan E Dosso and Michael J wilmut.Comparison of focalization and marginalization for Bayesian tracking in an uncertain ocean environment.J.Acoust.Soc.Am.,2009,125(2):717-722
[5]Stan E Dosso and Michael J wilmut,Bayesian source tracking via focalization and marginalization in an uncertain Mediterranean Sea environment,J.Acoust.Soc.Am.,2010,128(1):66-74
[6]李整林,郡锦,李风华等.由简正波群延时及幅度反演海底参数.声学学报,2002;27(6):487-491LI Zhenglin,YAN Jin,LI Fenghua.Inversion for the sea bottom acoustic parameters by using the group time delays and amplitude of normal mode,Acta Acustica,2002,27(6):487-491