A mobile localization algorithm based on fuzzy estimation for serious NLOS scenes

The indoor environment is intricate and the global positioning system (GPS) unable to satisfy the demand of indoor location accuracy. Therefore, the localization method based on wireless sensor network (WSN) has attached great importance and researched lately. The toughest issue to solve is the non-line of sight (NLOS) error caused by the uncertainty of the propagation environment. Hence, a location method based on hypothesis test and modified fuzzy probabilistic data association filter (HT-MFDAF) is proposed in this paper. Line-of-sight (LOS) and NLOS situations are regarded as an interactive Markov process. In the case of NLOS, we firstly identify and mitigate NLOS based on hypothesis testing theory. Then the ones which still have serious NOLS pollution is discarded by calculating similarity. Finally, the fuzzy membership degree is calculated by MFDAF, reconstructing the correlation probability to get the position estimate. The eventual location result is acquired by the Interactive Multiple Model (IMM) which weighted LOS and NLOS estimated position. Simulation and experimental results demonstrate the effectiveness of the algorithm.