煤炭工程 ›› 2020, Vol. 52 ›› Issue (5): 144-149.doi: 10.11799/ce202005030

• 研究探讨 • 上一篇    下一篇

井下WSN目标跟踪局部异常检测算法

金华明,郭倩倩,史明泉,赫佳星,崔丽珍   

  1. 1. 内蒙古科技大学
    2. 内蒙古自治区包头市昆都仑区阿尔丁大街7号内蒙古科技大学
  • 收稿日期:2019-06-17 修回日期:2019-09-04 出版日期:2020-05-15 发布日期:2020-07-23
  • 通讯作者: 郭倩倩 E-mail:2900205331@qq.com

Local anomaly detection algorithm for downhole WSN target tracking

  • Received:2019-06-17 Revised:2019-09-04 Online:2020-05-15 Published:2020-07-23

摘要: 针对煤矿井下特殊信道环境对无线传感器网络(WSN)目标跟踪造成的约束和对量测数据的精确性造成的影响,设计了适用于井下巷道特征的网络拓扑结构以及分布式分簇目标跟踪算法,并在此基础上提出运用局部异常因子检测算法(LOF)对量测数据中存在的异常点进行实时监测和更新最后结合交互式多模型滤波算法(IMM)实现目标状态估计,仿真结果表明,该算法有效提高了跟踪精度,平衡并降低了网络能耗。

关键词: WSN, 目标跟踪, 分布式分簇, LOF, IMM

Abstract: In view of the constraints caused by the special channel environment of coal mine on the target tracking of wireless sensor network (WSN) and the accuracy of measurement data, this paper designs the network topology and distributed clustering target suitable for the characteristics of underground roadway. Tracking algorithm, and based on this, the local anomaly factor detection algorithm (LOF) is used to monitor and update the wild value points existing in the measured data. Finally, the interactive multi-model filtering algorithm (IMM) is used to achieve the target state estimation. Simulation results analysis The effective balance of the algorithm reduces the network energy consumption and improves the tracking accuracy.

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