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