Maximum likelihood estimator for mobile location using time-of-arrival measurements based on accumulated error model

Localization of mobile station (MS) has now gained considerable attention. Many location methods have been proposed in the literature based on distance-free model. Although these algorithms have high positioning accuracy in the case of distance-free model, they may lose optimality in a real channel environment where range measurement noise is dependent on the true distance. In this paper, we propose a novel maximum likelihood estimator based on distance-dependent model for mobile location in a real environment which has not been studied in the literature. In addition, the Cramer-Rao lower bound (CRLB) is derived for distance-dependent model to indicate the best positioning accuracy. The simulations show that the proposed method outperforms the existing location methods and can reach the CRLB.