NLOS Identification and Mitigation for Time-based Indoor Localization Systems: Survey and Future Research Directions

Raphael Elikplim NKROW, Bruno SILVA, Dutliff BOSHOFF, Gerhard HANCKE, Mikael GIDLUND, Adnan ABU-MAHFOUZ

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

14 Citations (Scopus)
46 Downloads (CityUHK Scholars)

Abstract

One hurdle to accurate indoor localization using time-based networks is the presence of Non-Line-Of-Sight (NLOS) and multipath signals, affecting the accuracy of ranging in indoor environments. NLOS identification and mitigation have been studied over the years and applied to different time-based networks, with most works considering NLOS links with WiFi and UWB channels. In this paper, we discuss the effects and challenges of NLOS conditions on indoor localization and present current state-of-the-art approaches to NLOS identification and mitigation in literature. We survey these approaches and classify them under different categories together with their merits and demerits. We further categorize approaches to tackle NLOS effects into single and hybrid measurement-based approaches in this work. Lessons learnt from the survey with future directions are also presented in this paper. © 2024 The owner/author(s).
Original languageEnglish
Article number303
Number of pages41
JournalACM Computing Surveys
Volume56
Issue number12
Online published7 May 2024
DOIs
Publication statusPublished - Dec 2024

Funding

The work described in this paper was fully supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 11208122] and City University of Hong Kong [Project No. CityU 11212523].

Research Keywords

  • Time-of-Flight
  • NLOS
  • LOS
  • Ranging
  • Localization

Publisher's Copyright Statement

  • COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © Authors | ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Computing Surveys, https://doi.org/10.1145/3663473.

RGC Funding Information

  • RGC-funded

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