A Novel TOA-Based Mobile Localization Technique under Mixed LOS/NLOS Conditions for Cellular Networks

The presence of a non-Line-Of-Sight (NLOS) link between a base station (BS) and a mobile station (MS) in a cellular network is a major issue that limits the performance of the majority of time-Of-Arrival (TOA) localization methods. Due to blocking obstacles, a signal has to travel a longer distance to reach the other end of the communication link. Thus, the additional distance introduced by the presence of an NLOS link is usually modeled by a positive measurement bias. In contrast to most of relevant works that are either search based or iterative, in this paper, we propose a two-stage closed-Form estimator to localize an MS by three BSS in cellular networks. We use a distance-Dependent bias model to derive a range estimator as a first step. We then use trilateration to find an estimate of the MS position. To assess the performance of our technique, we derive the mean square error (MSE) of the estimator and numerically evaluate the Cramer-Rao lower bound (CRLB) as a benchmark. We investigate the performance of the proposed method under mixed line-Of-sight/NLOS scenarios in four environments, ranging from a bad urban environment to a rural environment. The provided Monte Carlo simulations show that our technique performs, on average, closely with the CRLB and provides localization capability with an average error of approximately 21 m in the worst environment among the four environments.