Auxiliary particle filter-based WLAN indoor tracking algorithm

Abstract

The particle filter (PF) has been implemented for location tracking in Wireless Local Area Network (WLAN) based indoor positioning system. However, the traditional PF technology is based on the sampling importance resampling (SIR), which has the inherent blindness. Therefore, its tracking performance in WLAN indoor environment is degraded. The auxiliary particle filter (APF) can solve this problem very well by making use of the current observation information during the production of new particles, so this paper employs the auxiliary particle filter (APF) for location tracking in WLAN fingerprinting positioning system to improve the WLAN indoor tracking performance. In the simulation, the weighted k-nearest neighbors method (WKNN) is chosen as the fingerprinting positioning algorithm. Simulation results show that APF based tracking algorithm performs better than PF based tracking algorithm. The APF based WLAN indoor tracking algorithm decreases the mean tracking error by 7.7% and 26.9% than PF based tracking algorithm and WKNN algorithm respectively.