An empirical ranging error model and efficient cooperative positioning for indoor applications

Shenghong Li; Mark Hedley; Iain B. Collings

doi:10.1109/ICCW.2015.7247275

An empirical ranging error model and efficient cooperative positioning for indoor applications

Shenghong Li, Mark Hedley, Iain B. Collings

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

3 Citations (Scopus)

Abstract

Distributed belief propagation is a promising technology for cooperative localization. Difficulties with belief propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. In this paper, we propose an efficient cooperative localization algorithm based on distributed belief propagation and a new empirical indoor ranging error model, which can be applied to indoor localization systems with non-Gaussian ranging error distributions. To reduce the communication overhead and computational complexity, the algorithm passes approximate beliefs represented by Gaussian distributions between neighbours and uses an analytical approximation to compute peer-to-peer messages. The proposed algorithm is validated on an indoor localization system deployed with 28 nodes covering 8000 m², and is shown to outperform existing algorithms.

Original language	English
Title of host publication	2015 IEEE International Conference on Communication Workshop, ICCW 2015
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	773-778
Number of pages	6
ISBN (Electronic)	9781467363051
DOIs	https://doi.org/10.1109/ICCW.2015.7247275
Publication status	Published - 8 Sep 2015
Event	IEEE International Conference on Communication Workshop, ICCW 2015 - London, United Kingdom Duration: 8 Jun 2015 → 12 Jun 2015

Other

Other	IEEE International Conference on Communication Workshop, ICCW 2015
Country/Territory	United Kingdom
City	London
Period	8/06/15 → 12/06/15

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10.1109/ICCW.2015.7247275

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title = "An empirical ranging error model and efficient cooperative positioning for indoor applications",

abstract = "Distributed belief propagation is a promising technology for cooperative localization. Difficulties with belief propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. In this paper, we propose an efficient cooperative localization algorithm based on distributed belief propagation and a new empirical indoor ranging error model, which can be applied to indoor localization systems with non-Gaussian ranging error distributions. To reduce the communication overhead and computational complexity, the algorithm passes approximate beliefs represented by Gaussian distributions between neighbours and uses an analytical approximation to compute peer-to-peer messages. The proposed algorithm is validated on an indoor localization system deployed with 28 nodes covering 8000 m2, and is shown to outperform existing algorithms.",

author = "Shenghong Li and Mark Hedley and Collings, {Iain B.}",

year = "2015",

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doi = "10.1109/ICCW.2015.7247275",

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pages = "773--778",

booktitle = "2015 IEEE International Conference on Communication Workshop, ICCW 2015",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "IEEE International Conference on Communication Workshop, ICCW 2015 ; Conference date: 08-06-2015 Through 12-06-2015",

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Li, S, Hedley, M & Collings, IB 2015, An empirical ranging error model and efficient cooperative positioning for indoor applications. in 2015 IEEE International Conference on Communication Workshop, ICCW 2015., 7247275, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 773-778, IEEE International Conference on Communication Workshop, ICCW 2015, London, United Kingdom, 8/06/15. https://doi.org/10.1109/ICCW.2015.7247275

An empirical ranging error model and efficient cooperative positioning for indoor applications. / Li, Shenghong; Hedley, Mark; Collings, Iain B.

2015 IEEE International Conference on Communication Workshop, ICCW 2015. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 773-778 7247275.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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N2 - Distributed belief propagation is a promising technology for cooperative localization. Difficulties with belief propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. In this paper, we propose an efficient cooperative localization algorithm based on distributed belief propagation and a new empirical indoor ranging error model, which can be applied to indoor localization systems with non-Gaussian ranging error distributions. To reduce the communication overhead and computational complexity, the algorithm passes approximate beliefs represented by Gaussian distributions between neighbours and uses an analytical approximation to compute peer-to-peer messages. The proposed algorithm is validated on an indoor localization system deployed with 28 nodes covering 8000 m2, and is shown to outperform existing algorithms.

AB - Distributed belief propagation is a promising technology for cooperative localization. Difficulties with belief propagation lie in achieving high accuracy without causing high communication overhead and computational complexity. In this paper, we propose an efficient cooperative localization algorithm based on distributed belief propagation and a new empirical indoor ranging error model, which can be applied to indoor localization systems with non-Gaussian ranging error distributions. To reduce the communication overhead and computational complexity, the algorithm passes approximate beliefs represented by Gaussian distributions between neighbours and uses an analytical approximation to compute peer-to-peer messages. The proposed algorithm is validated on an indoor localization system deployed with 28 nodes covering 8000 m2, and is shown to outperform existing algorithms.

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BT - 2015 IEEE International Conference on Communication Workshop, ICCW 2015

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An empirical ranging error model and efficient cooperative positioning for indoor applications

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