计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2464-2467.DOI: 10.11772/j.issn.1001-9081.2014.09.2464

• 网络与通信 • 上一篇    下一篇

基于二维网格融合特征参数的室内匹配定位算法

关维国1,鲁宝春2   

  1. 1. 辽宁工业大学 电子与信息工程学院,辽宁 锦州 121001;
    2. 辽宁工业大学 新能源学院,辽宁 锦州 121001
  • 收稿日期:2014-04-04 修回日期:2014-05-13 出版日期:2014-09-01 发布日期:2014-09-30
  • 通讯作者: 关维国
  • 作者简介: 
    关维国(1973-),男,辽宁锦州人,副教授,博士,主要研究方向:移动网络定位、通信信号处理;
    鲁宝春(1964-),男,辽宁锦州人,教授,博士,主要研究方向:电子技术。
  • 基金资助:

    辽宁省博士科研启动基金资助项目;辽宁省教育厅科学研究资助项目

Indoor matching localization algorithm based on two-dimensional grid characteristic parameter fusion

GUAN Weiguo1,LU Baochun2   

  1. 1. School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China
    2. School of New Energy, Liaoning University of Technology, Jinzhou Liaoning 121001, China
  • Received:2014-04-04 Revised:2014-05-13 Online:2014-09-01 Published:2014-09-30
  • Contact: GUAN Weiguo
  • Supported by:

    ;Liaoning Education Committee Foundation

摘要:

针对接收信号强度值(RSSI)的时变特性降低定位精度的问题,提出了一种基于二维网格特征参数融合的室内匹配定位算法。该算法融合RSSI和信号到达时间差(TDOA)构建网格特征参数模型,基于二维网格快速搜索策略降低匹配定位的计算量,采用网格特征向量的归一化欧氏距离进行最优网格匹配定位,最终由匹配网格的参考节点计算终端的精确位置。定位仿真实验中,该算法在3m网格粒度下的定位均方根误差为1.079m,平均定位误差小于1.865m;3m定位精度下的概率达到94.7%,相对于传统单一RSSI模型法提高了19.6%。所提算法能够有效提高室内定位精度,同时减少搜索数据量,降低匹配定位的计算复杂度。

Abstract:

Focused on the issue that the time-varying characteristic of indoor Received Signal Strength Indicator (RSSI) drastically degrades the localization accuracy, an indoor matching localization algorithm based on two-dimensional grid characteristic parameter fusion was proposed. The algorithm fused received signal strength and Time Difference of Arrival (TDOA) parameters to build grid feature model, in which two-dimensional grid quick search strategy was adopted to reduce computation amount. Normalized Euclidean distance of grid feature vector was used to realize the optimal grid match localization. Finally, the precise terminal location was computed by reference nodes of the matched grid. In the localization simulation experiments, the proposed algorithm achieved the localization Root Mean Square Error (RMSE) at 1.079m, and the average localization accuracy was within 1.865m in the condition of 3m grid granularity; The probability of 3m localization accuracy reached 94.7%, which was 19.6% higher than that of traditional method only bawsed on RSSI. The proposed algorithm can effectively improve the indoor positioning accuracy, meanwhile reduces the search data quantity and the computational complexity of matching localization.

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