A Novel WIFI-Oriented RSSI Signal Processing Method for Tracking Low-Speed Pedestrians

The accurate estimation of pedestrian movement location is one of hot research topics among Intelligent Transportation Systems. In the real-time pedestrian tracking application, the WiFi-based localization method is seriously affected by environmental noise, which results in the large positioning error. Aiming at this issue, this paper first analyzed the localization technologies based on the Received Signal Strength Indication (RSSI); and then proposed a fused algorithm by integrating constant speed filter and Kalman one, which could effectively suppress the positioning divergence caused by data acquisition error during positioning. Finally, in tests that used field data from the city of Guangzhou, China, the proposed algorithm showed promise in effectively reducing the average location error of 8.6% at the static environment and 11.43% at the dynamic one, respectively while comparing to the conventional Kalman filter.