基于RSSI和改进MCL算法的移动节点定位跟踪设计
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(河南牧业经济学院 信息工程系,郑州 450011)

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孔素真(1972-),女,河南滑县人,硕士,实验师,主要从事WSN定位技术方向的研究。 [FQ)]

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TP393

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河南省重点科技攻关计划项目(122102210487)。


Locating and Tracking for Mobile Sensor Node Based on RSSI and Improved MCL Algorism
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(Information Engineering Eepartment, Henan University of Animal Husbandry and Economy, Zhengzhou 450011,China)

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    摘要:

    由于传统节点定位方法大多针对静止传感器网络,不能适用于网络结构和节点位置动态变化的移动传感器网络,提出了一种基于RSSI测距和改进的MCL (Monte Carlo Localization)算法的移动传感器节点定位跟踪方法;首先描述了经典MCL算法和接收信号强度RSSI测距方法,然后设计了一种改进的MCL算法,将传统的MCL方法预测粒子位置的过程即预测和滤波两个阶段,更新为锚节点TTL受控泛洪方式广播自身位置、采用拉格朗日插值法预测节点下一时刻的位置和速度、求取锚盒采样区域、k 跳锚节点粒子滤波和根据预测下一时刻的节点位置和速度与当前时刻的位置信息确定各粒子权重的5个阶段;采用仿真器MCL-Simulator进行仿真,结果证明:文中方法能有效实现移动节点的定位,与其它方法相比,具有较小的平均定位误差,具有很强的可行性。

    Abstract:

    Aiming at the traditional localization method usually aiming at the static wireless sensor network, can not suit the mobile sensor network with dynamic changing network structure and node distance, a locating and tracking method for mobile sensor node was proposed based on RSSI and improved MCL. Firstly, the RSSI ranging distance method and classic MCL was introduced, then an improved MCL algorithm was proposed by renewing the process of classic MCL such as predicating and filtering to five stages as anchor node broadcast its position using limit TTL flooding protocol, using Lagrange interpolation to predict the position and speed in the next time, obtaining anchor sampling area, particle filter and according the predicting position and speed in the next time and the current position to weight particle. The experiment was simulated in the MCL-Simulator shows the method in this paper can realize the node moving, compared with the other methods, it has less average locating error, so it has big feasibility.

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孔素真,孙雅娟.基于RSSI和改进MCL算法的移动节点定位跟踪设计计算机测量与控制[J].,2014,22(5):1637-1639,1643.

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  • 收稿日期:2014-01-02
  • 最后修改日期:2014-02-22
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  • 在线发布日期: 2014-12-16
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