Non-Line-of-Sight Identification Without Channel Statistics

Bruno J. Silva; Gerhard P. Hancke

doi:10.1109/IECON43393.2020.9255110

Non-Line-of-Sight Identification Without Channel Statistics

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

16 Citations (Scopus)

Abstract

Identifying non-line-of-sight (NLOS) conditions is important to discard, or improve, any location estimates that have been estimated with NLOS ranges. Typically, NLOS identification relies on channel statistics that have been collected for both LOS and NLOS channels. We investigate NLOS identification using distance residuals instead. The results show that distance residuals can be used to identify location estimates with NLOS ranges with very high accuracy, and that in some cases, individual NLOS ranges can also be identified.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Pages	4489-4493
ISBN (Electronic)	978-1-7281-5414-5
ISBN (Print)	978-1-7281-5415-2
DOIs	https://doi.org/10.1109/IECON43393.2020.9255110
Publication status	Published - Oct 2020
Event	46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020) - Virtual, Singapore Duration: 19 Oct 2020 → 21 Oct 2020 https://www.iecon2020.org/

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	1553-572X
ISSN (Electronic)	2577-1647

Conference

Conference	46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020)
Place	Singapore
Period	19/10/20 → 21/10/20
Internet address	https://www.iecon2020.org/

Research Keywords

localization
non-line-of-sight
wireless sensor networks

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10.1109/IECON43393.2020.9255110

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@inproceedings{8b17b37bbc3b456887fc759977399efe,

title = "Non-Line-of-Sight Identification Without Channel Statistics",

abstract = "Identifying non-line-of-sight (NLOS) conditions is important to discard, or improve, any location estimates that have been estimated with NLOS ranges. Typically, NLOS identification relies on channel statistics that have been collected for both LOS and NLOS channels. We investigate NLOS identification using distance residuals instead. The results show that distance residuals can be used to identify location estimates with NLOS ranges with very high accuracy, and that in some cases, individual NLOS ranges can also be identified.",

keywords = "localization, non-line-of-sight, wireless sensor networks",

author = "Silva, {Bruno J.} and Hancke, {Gerhard P.}",

year = "2020",

month = oct,

doi = "10.1109/IECON43393.2020.9255110",

language = "English",

isbn = "978-1-7281-5415-2",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE",

pages = "4489--4493",

booktitle = "Proceedings",

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note = "46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020) ; Conference date: 19-10-2020 Through 21-10-2020",

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Silva, BJ & Hancke, GP 2020, Non-Line-of-Sight Identification Without Channel Statistics. in Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society., 9255110, IECON Proceedings (Industrial Electronics Conference), IEEE, pp. 4489-4493, 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020), Singapore, 19/10/20. https://doi.org/10.1109/IECON43393.2020.9255110

Non-Line-of-Sight Identification Without Channel Statistics. / Silva, Bruno J.; Hancke, Gerhard P.
Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. p. 4489-4493 9255110 (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Non-Line-of-Sight Identification Without Channel Statistics

AU - Silva, Bruno J.

AU - Hancke, Gerhard P.

PY - 2020/10

Y1 - 2020/10

N2 - Identifying non-line-of-sight (NLOS) conditions is important to discard, or improve, any location estimates that have been estimated with NLOS ranges. Typically, NLOS identification relies on channel statistics that have been collected for both LOS and NLOS channels. We investigate NLOS identification using distance residuals instead. The results show that distance residuals can be used to identify location estimates with NLOS ranges with very high accuracy, and that in some cases, individual NLOS ranges can also be identified.

AB - Identifying non-line-of-sight (NLOS) conditions is important to discard, or improve, any location estimates that have been estimated with NLOS ranges. Typically, NLOS identification relies on channel statistics that have been collected for both LOS and NLOS channels. We investigate NLOS identification using distance residuals instead. The results show that distance residuals can be used to identify location estimates with NLOS ranges with very high accuracy, and that in some cases, individual NLOS ranges can also be identified.

KW - localization

KW - non-line-of-sight

KW - wireless sensor networks

UR - http://www.scopus.com/inward/record.url?scp=85097766663&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85097766663&origin=recordpage

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DO - 10.1109/IECON43393.2020.9255110

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-7281-5415-2

T3 - IECON Proceedings (Industrial Electronics Conference)

SP - 4489

EP - 4493

BT - Proceedings

PB - IEEE

T2 - 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020)

Y2 - 19 October 2020 through 21 October 2020

ER -

Non-Line-of-Sight Identification Without Channel Statistics

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