WIFI Indoor Positioning Algorithm Based on Improved Kalman Filtering

Indoor positioning technology based on WiFi signal has advantages of wide range of use, low cost, portability and becomes a research hotspot in field of indoor position sensing. On the basis of analyzing and summarizing the existing methods that reduce the influence of errors on location fingerprint algorithm, we propose a kind of improved location fingerprint algorithm. Weighted fuzzy matching algorithm is used to improve positioning accuracy. Then improved Kalman filtering based on particle swarm optimization is used in error correction. The experiment results show that the proposed scheme has lower average error than several traditional methods.