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水声传感器网络移动节点定位技术
引用本文:付广义,曹利,李峥,李宇,张春华.水声传感器网络移动节点定位技术[J].声学技术,2014,33(2):108-112.
作者姓名:付广义  曹利  李峥  李宇  张春华
作者单位:中国科学院声学研究所,北京100190
摘    要:针对水声传感器网络的移动节点定位问题,首先研究了基于距离测量值的多边定位方法(Multilateral Localization,ML);然后利用节点运动信息,提出采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)进行跟踪的方法;最后针对水下移动节点的测量值不同步问题,提出了修正扩展卡尔曼滤波(Modified Extend Kalman Filter,MEKF)以改进EKF的精度。仿真分析结果表明,MEKF的定位精度要好于EKF,而EKF和MEKF由于其用到了节点的运动信息,因此其定位精度要远好于ML。

关 键 词:水声传感器网络  节点定位  扩展卡尔曼滤波  修正扩展卡尔曼滤波
收稿时间:2012/11/12 0:00:00
修稿时间:2013/1/18 0:00:00

Study of mobile node localization in underwater acoustic sensor network
FU Guang-yi,CAO Li,LI Zheng,LI Yu and ZHANG Chun-hua.Study of mobile node localization in underwater acoustic sensor network[J].Technical Acoustics,2014,33(2):108-112.
Authors:FU Guang-yi  CAO Li  LI Zheng  LI Yu and ZHANG Chun-hua
Affiliation:(Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China)
Abstract:Aiming at the problem of mobile node localization in underwater acoustic sensor network,this paper first studies the multilateral localization method based on distance measurements in detail.Then this paper presents a method of using extended Kalman filter (EKF) with the motion information to track the mobile nodes.Finally,the modified extend Kalman filter (MEKF) is proposed to improve EKF because the measurements of mobile nodes are asynchronous.The simulation study indicates that the localization accuracy of MEKF is much better than that of EKF,and EKF performs better than ML.
Keywords:underwater acoustic sensor network  node localization  extended Kalman filter  modified extend Kalman filter
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