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  • ISSN 1006-3080
  • CN 31-1691/TQ

一种基于事件触发的异构WSN分布式定位方法

范玮 刘济

范玮, 刘济. 一种基于事件触发的异构WSN分布式定位方法[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220211001
引用本文: 范玮, 刘济. 一种基于事件触发的异构WSN分布式定位方法[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220211001
FAN Wei, LIU Ji. An event-triggered distributed positioning method of a heterogeneous WSN[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220211001
Citation: FAN Wei, LIU Ji. An event-triggered distributed positioning method of a heterogeneous WSN[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220211001

一种基于事件触发的异构WSN分布式定位方法

doi: 10.14135/j.cnki.1006-3080.20220211001
基金项目: 国家自然科学基金项目(61971278)
详细信息
    作者简介:

    范玮:作者简介:范 玮(1998-),女,山东潍坊人,硕士生,主要研究方向:室内定位、容积信息滤波。E-mail:15216886252@163.com

    通讯作者:

    刘 济, E-mail:jiliu@ecust.edu.cn

  • 中图分类号: TN929.5;TP212.9

An event-triggered distributed positioning method of a heterogeneous WSN

  • 摘要: 由于电磁波传输的多径效应和信号干扰,基于RSS测距的室内定位精度较低,本文提出一种融合RSS和惯性测量的异构无线传感器网络(WSN)室内定位方案,采用基于位置估计信任度的分布式一致性容积信息滤波算法来协同估计目标的位置。引入事件触发机制,基于RSS信号强度触发WSN节点唤醒,以提高网络的抗干扰性和降低网络能耗。室内移动小车的定位仿真和实验测试结果表明,所提出的WSN融合定位精度明显优于单一定位方法,具有显著的抗干扰性能,分布式定位和事件触发机制可有效地降低网络能耗。

     

  • 图  1  定位方案

    Figure  1.  Position scheme

    图  2  异构WSN定位系统的多传感器多层级融合体系

    Figure  2.  Multi-sensor multi-level fusion system for heterogeneous WSN positioning system

    图  3  仿真环境和条件

    Figure  3.  Simulation environment and conditions

    图  4  #18和#25节点的原始和受干扰RSS数据

    Figure  4.  Raw and disturbed RSS data for nodes #18 and # 25

    图  5  室内小车定位仿真轨迹

    Figure  5.  Indoor vehicle positioning simulation trajectory

    图  6  事件触发机制对定位误差的影响

    Figure  6.  Influence of event triggering mechanism on positioning error

    图  7  事件触发机制下的WSN服务节点图

    Figure  7.  WSN service node graph under event triggered mechanism

    图  8  实验场景及实验设备

    Figure  8.  Experimental scene and equipment

    图  9  实验室RSS功率与距离模型拟合数据

    Figure  9.  Fitting data of RSS power and distance model in laboratory

    图  10  IMU模块计算的观测数据

    Figure  10.  Observation data calculated by IMU module

    图  11  不同定位方案的RMSE曲线

    Figure  11.  RMSE curves of different location schemes

    图  12  定位结果及各轨迹段服务节点示意

    Figure  12.  positioning result and service node indication of each track segment

    表  1  算法在各种电磁干扰环境下的定位误差

    Table  1.   The positioning error of the algorithm in various electromagnetic interference environments

    DC
    interference
    AC
    interference
    Random noise
    interference
    MAE/mAverage0.2640.2500.298
    Maximum0.9170.9021.076
    RMSE/mAverage0.2700.2530.315
    Maximum0.9350.9121.104
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出版历程
  • 收稿日期:  2022-02-11
  • 网络出版日期:  2022-04-25

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