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.