Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (4): 1198-1201.DOI: 10.11772/j.issn.1001-9081.2017.04.1198

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Integrated indoor positioning algorithm based on D-S evidence theory

WANG Xuqiao, WANG Jinkun   

  1. Robotics Institute, Civil Aviation University of China, Tianjin 300300, China
  • Received:2016-08-04 Revised:2016-12-27 Online:2017-04-10 Published:2017-04-19
  • Supported by:
    This work is partially supported by Major Program of Natural Science Foundation of Tianjin (12JCZDJC34200).


王续乔, 王瑾琨   

  1. 中国民航大学 机器人研究所, 天津 300300
  • 通讯作者: 王续乔
  • 作者简介:王续乔(1983-),男,吉林吉林人,实验师,硕士,主要研究方向:无线传感器网络、智能系统;王瑾琨(1990-),男,河北廊坊人,硕士研究生,主要研究方向:智能优化算法。
  • 基金资助:

Abstract: An integrated positioning algorithm for Wireless Fidelity / Inertial Measurement Unit (WiFi/IMU) based on D-S evidence inference theory was proposed for large indoor area Location Based Service (LBS) without beacons deployment. Firstly, the transmission model of signal strength of a single Access Point (AP) was established, then Kalman Filter was used to denoise the Received Signal Strength Indication (RSSI). Secondly, Dempster/Shafer (D-S) evidence theory was applied in the data fusion process for real-time acquisition of multi-sources, including the signal strength of WiFi, yaw and accelerations on all shafts; then the fingerprint blocks with high confidence were selected. Finally, the Weighted K-Nearest Neighbor (WKNN) method was exploited for the terminal position estimation. Numerical simulations on unit area show that the maximum error is 2.36 m and the mean error is 1.27 m, which proves the viability and effectiveness of the proposed algorithm; the cumulated error probability is 88.20% when the distance is no greater than the typical numerical value, which is superior to 70.82% of C-Support Vector Regression (C-SVR) or 67.85% of Pedestrian Dead Reckoning (PDR). Furthermore, experiments on the whole area of the real environment also show that the proposed algorithm has an excellent environmental applicability.

Key words: Wireless Local Area Network (WLAN), indoor positioning, Received Signal Strength Indication (RSSI), location fingerprint, Dempster/Shafer (D-S) evidence theory, Weighted K-Nearest Neighbor (WKNN)

摘要: 在非定位系统部署信标的大体量场区环境下,针对基于位置的服务(LBS)的室内定位需求问题,提出了一种基于D-S证据推理理论的无线局域网/惯性测量组件(WiFi/IMU)组合定位算法。该算法首先建立各接入点(AP)单点的信号强度传输模型,并利用卡尔曼滤波对接收到的信号强度指示(RSSI)值进行去噪修正处理;然后通过D-S证据理论对实时采集的WiFi信号强度、偏航角、各轴加速度的多源信息进行融合处理,选取可信度高的指纹区块;最后通过加权K近邻(WKNN)算法得到终端估算位置。单元场区仿真实验结果显示,最大误差2.36 m,综合平均误差1.27 m,验证了该算法的可行性与有效性;且误差累计概率分布在小于等于典型距离时为88.20%,优于惩罚参数C支持向量回归机(C-SVR)的70.82%和行人航迹推算(PDR)算法的67.85%。进一步地,算法在全场区实际实验中也表现出了良好的环境适用性。

关键词: 无线局域网, 室内定位, 接收信号强度指示, 位置指纹, D-S证据理论, 加权K近邻

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