TDOA-Based Localization for Semi-Static Targets in NLOS Environments

Shenghong Li; Mark Hedley; Iain B. Collings; David Humphrey

doi:10.1109/LWC.2015.2449306

TDOA-Based Localization for Semi-Static Targets in NLOS Environments

Shenghong Li, Mark Hedley, Iain B. Collings, David Humphrey

School of Engineering

Research output: Contribution to journal › Article

21 Citations (Scopus)

Abstract

We study the problem of locating semi-static targets in non-line-of-sight (NLOS) environments using multiple sets of time-difference-of-arrival (TDOA) measurements. Specific focus is placed on the correlation between TDOA measurements caused by NLOS errors. We propose an algorithm which takes this correlation into account when fusing the available TDOA measurements and achieves high accuracy in estimating both the position and the associated uncertainty. In addition, a statistical ranging error model for NLOS environments is proposed based on empirical ranging data, which substantially improves the positioning accuracy. The proposed approach is verified experimentally in an aircraft cabin and achieves seat-level accuracy for 99% of the nodes.

Original language	English
Article number	7132722
Pages (from-to)	513-516
Number of pages	4
Journal	IEEE Wireless Communications Letters
Volume	4
Issue number	5
DOIs	https://doi.org/10.1109/LWC.2015.2449306
Publication status	Published - 1 Oct 2015

Keywords

Accuracy
Correlation
Distance measurement
Measurement uncertainty
Noise measurement
Position measurement
Wireless communication

Access to Document

10.1109/LWC.2015.2449306

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title = "TDOA-Based Localization for Semi-Static Targets in NLOS Environments",

abstract = "We study the problem of locating semi-static targets in non-line-of-sight (NLOS) environments using multiple sets of time-difference-of-arrival (TDOA) measurements. Specific focus is placed on the correlation between TDOA measurements caused by NLOS errors. We propose an algorithm which takes this correlation into account when fusing the available TDOA measurements and achieves high accuracy in estimating both the position and the associated uncertainty. In addition, a statistical ranging error model for NLOS environments is proposed based on empirical ranging data, which substantially improves the positioning accuracy. The proposed approach is verified experimentally in an aircraft cabin and achieves seat-level accuracy for 99{\%} of the nodes.",

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AU - Collings, Iain B.

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KW - Correlation

KW - Distance measurement

KW - Measurement uncertainty

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