A Mobile Location Algorithm with Least Range and Clustering Techniques for NLoS Environments

We propose an efficient location algorithm which can mitigate the influence of NLOS error. Based on the geometric relationship between known positions of the base stations, the theorem of "Fermat Point" is utilized to collect the candidate positions (CPs) of the mobile station. Then, a set of weighting parameters are computed using a density-based clustering method. Finally, the location of mobile station is estimated by solving the optimal solution of the weighted objective function. Different distributions of NLOS error models are used to evaluate the performance of this method. Simulation results show that the performance of the least range measure (LRM) algorithm is slightly better than density-based clustering algorithm (DCA), and superior to the range based linear lines of position algorithm (LLOP) and range scaling algorithm (RSA) on location accuracy under different NLOS environments. The simulation results also satisfy the location accuracy demand of Enhanced 911 (E-911).