Robust wireless positioning in urban environments via validated data, modeling and fuzzy inferences

To enhance the accuracy and robustness of wireless positioning in mixed line-of-sight (LOS)/non-LOS (NLOS) Manhattan-like street environments, we present a selective fuzzy-tuned extended Kalman filtering based interacting multiple-model (SFT-IMM-EKF) estimation algorithm for mobile localization. For the development of the proposed model-based algorithm, we introduce validated range and signal power measurements in which NLOS bias effects are limited under small/large scale multipath fading. Moreover, we propose a Markov state transitioned hybrid system framework with aggregate validated measurement data, NLOS modeling and fuzzy inferences for modeling the motion dynamics of a mobile station with respect to each base station. Simulations demonstrate that the proposed algorithm can reliably achieve much better localization accuracy and robustness than several existing methods.