A non-line-of-sight error mitigation method for location estimation

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.

NLOS Error Mitigation in Cellular Positioning using PSO Optimization Algorithm

“…In particular, the simulation was performed for cellular configuration with three BSs (BS1, BS2 and BS3) and better results were obtained than those mentioned in a similar study [22]. The location accuracy is measured in terms of Root Mean Square Error (RMSE) between the actual MS location and the estimated MS location.…”

“…In practice, the NLOS distance error is often modeled as a random variable that is uniformly distributed in the interval [ min , max ], (we denote it as ~U( min , max )), with the following probability density function [13,22]:…”

“…Generally, it is very difficult to find the optimal solution of the NLS optimization problem (4), and one of the most known algorithms that tries to achieve this is the gradientbased Levenberq-Marquardt (LM) method. To solve (4), many studies have proposed near-optimal searching methods based on swarm intelligence, such as the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm [22]. Due to its good properties, in this paper we suggest the PSO algorithm to minimize the given object function and estimate the MS location.…”

“…A new localization scheme based on the metaheuristic concept of optimization uses the Hybrid Taguchi-Genetic Algorithm (HTGA) [21] and the Particle Swarm Optimization (PSO) algorithm presented in [22][23][24][25]. In this paper we propose an efficient method to reduce the NLOS effect where the TOA measurement error is assumed to be uniformly distributed.…”

Location Information Quality: A Review

“…When this does not happen due to obstructions, refraction or reflection, we call it Non Line of Sight (NLOS) propagation. Significant errors in time and angle measurements occur because of NLOS propagation [ 77 ]. Some localization technologies require LOS propagation for accurate localization (e.g., IR) but in urban areas or indoor environments, the direct LOS path is often blocked due to dense buildings, bridges, concrete structures or furniture.…”