基于D-S证据理论的室内组合定位算法

Integrated indoor positioning algorithm based on D-S evidence theory

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1.27m,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。

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.