Abstract: In order to improve the accuracy and stability of indoor positioning, an indoor localization algorithm using particle filtering to fuse WiFi fingerprinting and Pedestrian Dead Reckoning (PDR) was proposed. To reduce the negative influence of complex indoor environment on WiFi fingerprinting, a Support Vector Machine (SVM)-based WiFi fingerprinting algorithm using SVM classification and regression for more accurate location estimation was proposed. For smartphone based PDR, in order to reduce the error of inertial sensor, and the effects of random walk, the method of state transition was used to recognize the gait cycles and count the steps, the parameters of state transition were set dynamically using real-time acceleration data, the step length was calculated with Kalman filtering by making use of the relationship between vertical acceleration and step size, and the relationship between adjacent step sizes. The experimental results show that SVM-based WiFi fingerprinting outperformed Nearest Neighbor (NN) algorithm by 34.4% and K-Nearest Neighbors (KNN) algorithm by 27.7% in average error distance, the enhanced PDR performed better than typical step detection software and step length estimation algorithms. After particle filtering, the trajectory of the fused solution is closer to the real trajectory than WiFi fingerprinting and PDR. The average error distance of linear walking is 1.21 m, better than 3.18 m of WiFi and 2.76 m of PDR; the average error distance of a walking through several rooms is 2.75 m, better than 3.77 m of WiFi and 2.87 m of PDR.

Key words: indoor positioning, multi-sensor fusion, particle filtering, Pedestrian Dead Reckoning (PDR), WiFi fingerprinting, Support Vector Machine (SVM)

摘要： 为提高室内定位的精度和稳定性,提出使用粒子滤波融合WiFi指纹定位和行人航位推算的室内定位算法。为减少复杂室内环境对WiFi指纹定位的影响,提出将支持向量机分类与回归相结合的两级WiFi指纹定位算法。在基于智能手持设备惯性传感器的行人航位推算中,为减少惯性传感器的误差以及人随意行走带来的影响,采用状态转换的方法识别行走周期并进行步数统计,提出根据实时加速度数据动态设置状态转换的参数,利用步长和垂直加速度之间的关系以及相邻步长之间的关系,应用卡尔曼滤波进行步长计算。仿真实验中,基于支持向量机的WiFi指纹定位的平均误差比最近邻居(NN)算法降低34.4%,比K最近邻居(KNN)算法降低27.7%。改进的行人航位推算的性能优于常用代表性计步软件和步长计算算法,而经过粒子滤波融合后估计的行走轨迹更加接近真实轨迹:直线行走平均误差为1.21 m,优于WiFi的3.18 m和航位推算的2.76 m;曲线行走平均误差为2.75 m,优于WiFi的3.77 m和航位推算的2.87 m。

关键词: 室内定位, 多传感器融合, 粒子滤波, 行人航位推算, WiFi指纹, 支持向量机