Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization

Abstract

Received Signal Strength Indicator (RSSI) is affected significantly by multi-path fading, building structure and obstacles in indoor environments, which lead to similar fingerprints problem and noise. To improve the performance of traditional fingerprinting method, the measurements provided by inertial sensors can be leveraged. Particle filter (PF) method is a widely chosen algorithm for sensor fusion. However, the initialization and weighting process are problematic in indoor positioning systems. This paper proposes a new PF scheme which yield a smooth and stable localization experience. To differentiate similar fingerprints, a single-hidden layer feed-forward networks (SLFNs) is used to model the multiple probabilistic estimations and improve the performance of the PF. Meanwhile, a new initialization algorithm using Random Sample Consensus (RANSAC) is presented to reduce the convergence time. Experimental measurements were carried out to determine the performance of the proposed algorithm. The results indicate that the positioning error of proposed scheme falls to less than 1.2 m which is better than the error reported in comparable approaches.