Non-Line-of-Sight Identification Without Channel Statistics

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

8 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers
Pages4489-4493
ISBN (Electronic)978-1-7281-5414-5
ISBN (Print)978-1-7281-5415-2
Publication statusPublished - Oct 2020

Publication series

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

Conference

Title46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020)
LocationVirtual
PlaceSingapore
Period19 - 21 October 2020

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.

Research Area(s)

  • localization, non-line-of-sight, wireless sensor networks

Citation Format(s)

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. Institute of Electrical and Electronics Engineers, 2020. p. 4489-4493 9255110 (IECON Proceedings (Industrial Electronics Conference)).

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