Robust urban mobile localization using data fusion and intelligent estimation

In this paper, we utilize the synergy between data aggregation and fuzzy inferences to derive a new intelligent multiple-model-based estimation algorithm for robust urban mobile localization. The location estimation problem is cast into a Markov state transitioned system framework with fuzzy inferences for each base station for modeling the dynamics of a mobile station (MS), and data fusion of received time-of-arrival data and signal strength. This leads to a fuzzy-tuned extended Kalman filtering based interacting multiple-model (FT-IMM-EKF) algorithm. Due to data fusion along with an adequate fuzzy adaption of process noise variances, the proposed FT-IMM-EKF can ameliorate adverse effects of both NLOS errors and MS mobility more effectively than other relevant methods. The analytical results are substantiated by simulations.