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.
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2016, Vol. 36 
