Improved Localization Algorithms Based on Reference Selection of Linear Least Squares in LOS and NLOS Environments

Authors:
Shixun Wu;Jiping Li;Shouyin Liu
Affiliations:
Department of Electronics and Information Engineering, Central China Normal University, Wuhan, China 430079;Department of Electronics and Information Engineering, Central China Normal University, Wuhan, China 430079;Department of Electronics and Information Engineering, Central China Normal University, Wuhan, China 430079
Venue:
Wireless Personal Communications: An International Journal
Year:
2013

Citing 5
Cited 1

A linear programming approach to NLOS error mitigation in sensor networks

Proceedings of the 5th international conference on Information processing in sensor networks
Robust statistical methods for securing wireless localization in sensor networks

IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Joint synchronization and localization using TOAs: a linearization based WLS solution

IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
A simple and efficient estimator for hyperbolic location

IEEE Transactions on Signal Processing
Analysis of wireless geolocation in a non-line-of-sight environment

IEEE Transactions on Wireless Communications

A Non-Line-of-Sight mitigation localization algorithm for sensor networks using clustering analysis

Computers and Electrical Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Linear Least Squares (LLS) estimation is a low complexity but sub-optimum method for estimating the location of a mobile terminal (MT) from some measured distances. It requires selecting one of the known fixed terminals (FTs) as a reference FT for obtaining a linear set of expressions. In this paper, the choosing of the reference FT is investigated. By analyzing the objective function of LLS algorithm, a new method for selecting the reference FT is proposed, which selects the reference FT based on the minimum residual (denoted as MR-RS) rather than the smallest measured distance and improves the localization accuracy significantly in Line of sight (LOS) environment. In Non-line of sight (NLOS) environment, we combine MR-RS algorithm with two other existing algorithms (residual weighting algorithm and three-stage algorithm) to form new algorithms, which also improve the localization accuracy comparing with the two algorithms. Moreover, the time complexity of the proposed algorithms is analyzed. Simulation results show that the proposed methods are always better than the existing methods for arbitrary geometry position of the MT and the LOS/NLOS conditions.