Robust mobile location estimator with NLOS mitigation using interacting multiple model algorithm

A Kalman-based Interacting Multiple Model (IMM) smoother is proposed for mobile location estimation with the time of arrival (TOA) measurement data in cellular networks to meet the Federal Communications Commission (FCC) requirement for phase I In this study, the line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in cellular networks are considered as a Markov process with two interactive modes. Then we propose a Kalman-based IMM smoother to accurately estimate smooth range between the corresponding base station (BS) and mobile station (MS) in cellular networks. It is shown that the proposed mobile location estimator can efficiently mitigate the NLOS effects of the measurement range error even when the corresponding BS changes the condition between LOS and NLOS. Simulation results demonstrate that the performance of the proposed Kalman-based IMM smoother is improved significantly over the FCC target in both fixed LOS/NLOS and LOS/NLOS transition conditions.