NLOS Error Mitigation in Cellular Positioning Using PSO Optimization Algorithm

Abstract

Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore, many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presents the procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sight propagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. In computer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrival test measurements using the four base stations and for simulation selects the measurement combination that give the smallest region enclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, and therefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem with the corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobile station location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines the mobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as the Weighted Least Squares and the Levenberq-Marquardt method.