Abstract
Because there are always non-line-of-sight effects in signal propagation, researchers have proposed various algorithms to mitigate the measured error caused by non-line-of-sight. Initially inspired by flocking birds, particle swarm optimization is an evolutionary computation tool for optimizing a problem by iteratively attempting to improve a candidate solution with respect to a given measure of quality. In this article, we propose a new location algorithm that uses time-of-arrival measurements to improve the mobile station location accuracy when three base stations are available. The proposed algorithm uses the intersections of three time-of-arrival circles based on the particle swarm optimization technique to give a location estimation of the mobile station in non-line-of-sight environments. An object function is used to establish the nonlinear relationship between the intersections of the three 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 algorithm performs better than the related algorithms in wireless positioning systems, even in severe non-line-of-sight propagation conditions.
Highlights
The requirement for more accurate position determination has extensively increased
This article presents a new positioning algorithm based on the particle swarm optimization (PSO) technique to determine the mobile stations (MSs) location in NLOS environments
PSO is an evolutionary computation that imitates the movement of organisms such as a flock of birds or a school of fish
Summary
The requirement for more accurate position determination has extensively increased. To verify the performance of the proposed PSO-based algorithm in detail, different NLOS error models are demonstrated. The simulation results show that the proposed PSO-based algorithm provides a much better location estimation than the Taylor series algorithm (TSA),[27,28] linear lines of position (LLOP) algorithm,[29] and rangescaling algorithm (RSA).[14]
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More From: International Journal of Distributed Sensor Networks
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