An Indoor Positioning System Based on Map-Aided KF-PF Module

Due to the drift of yaw, low accuracy and accumulative erroring in the procedure of using smartphone to realize Pedestrain dead reckoning algorithm, a map-aided KF-PF multi-filter algorithm is used to optimize PDR algorithm. Based on the traditional PDR algorithm, a Kalman Filter, fusing output of gyroscope and cartographic information primarily, is used to get the orientation, then using the map-matching particle filter to process the route results. The experimental results show that the flexibility of indoor positioning is improved, in the meanwhile, the stability and preciseness of the positioning results is enhanced and the algorithm can eliminate the error of the drift of yaw. Compared with the traditional particle filter, the map-matching particle filter can decrease the number of particles and computation burden effectively, which makes the possibility for realizing the real-time indoor localization.