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基于移动信标的无线传感器网络节点定位 被引量：32

Node Localization of Wireless Sensor Networks Based on Mobile Beacon
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摘要 提出了一种基于测距的移动信标节点定位方法。该方法采用一个移动信标节点,信标的移动方式采用高斯马尔可夫模型,该移动模型基本能遍历整个传感区域,能使未知节点获得足够定位信息;定位机制采用加权质心与扩展卡尔曼滤波算法相结合,先用加权质心算法进行初精度定位,进而用扩展卡尔曼滤波方法进行精确定位。通过对信标移动时间、移动速度、虚拟信标点个数、迭代次数等定位参数分别进行仿真试验,实验结果表明在相同条件下该方法与最小二乘法(LSE)及加权最小二乘法(WLSE)相比,其定位精度高,健壮性好,可扩展性强。 A mobile beacon localization algorithm is proposed. The algorithm uses a mobile beacon The mobility method of the mobile beacon uses Gaussian Markov Model, so Sufficient localization information can be obtained for each unknown node. The algorithm combines weighted centroid method and Extended Kalman Filter. Firstly, weighted centroid method is used to get the initial estimated value, then the EKF is adopted to circulate and optimize. The simulation is carried according to different beacon number, itera- tire times, different moving time and different moving speed of the beacon. In addition, the comparison of the localization effectiveness among the Least Squares Estimation Algorithms （LSE）, Weighted Least Squares Estimation Algorithms（WLSE） and the proposed algorithm shows that the proposed algorithm has high precision, low computing complexity and strong expanded ability.
作者 陈娟 李长庚 宁新鲜
机构地区 中南大学物理科学与技术学院
出处 《传感技术学报》 CAS CSCD 北大核心 2009年第1期121-125,共5页 Chinese Journal of Sensors and Actuators
关键词 无线传感器网络 节点定位 高斯马尔科夫模型 扩展卡尔曼滤波 wireless sensor networks node localizatiom gaussian markov model extended kalman filter
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

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二级参考文献70

  • 1王福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J].软件学报,2005,16(5):857-868. 被引量：671
  • 2汪琼,张锋.无线传感器网络中的节点协作算法研究[J].传感技术学报,2006,19(2):481-485. 被引量：4
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共引文献721

同被引文献288

引证文献32

二级引证文献272

传感技术学报
2009年 第1期

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