Location-constrained Particle Filter for Rssi-based Indoor Human Positioning and Tracking System

This paper proposes a Location-Constrained Particle Filter (LC-PF) for Radio Signal Strength Indication (RSSI) based indoor localization system. Based on proposed LC-PF, the RSSI fluctuation problem can be restrained. The proposed methods include locationconstrained importance weight updating (LC-WU) and location-constrained propagation model (LC-model). LC-WU eliminates particles in prohibited regions based on the geolocation of the map. The LC-model propagates particles based on different turning probabilities in different regions. These two methods can be applied separately or jointly. The proposed LC-PF has 2.48m average accuracy improvement over basic PF with 68% error reduction, and results in 2.07m accuracy with 90% confidence.