Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Cam Ly Nguyen, Orestis Georgiou, Vorapong Suppakitpaisarn

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

Wireless localization methods are often subject to errors due to radio signal fluctuations that are used to estimate inter-device separation distances. We propose a novel method called MLRefine to counter these effects by refining RSS measurement data to obtain more accurate values that can enhance ranging and localization accuracies. MLRefine uses machine learning methods to model the relationship between accurate values and features extracted from in silico RSS values. MLRefine then applies the trained model to features extracted from real RSS measurement values to return a predicted set of refined RSS values. The refined RSS values are shown through computer simulations and real experiments to improve localization accuracy.

Original languageEnglish
Title of host publication2018 IEEE Globecom Workshops, GC Wkshps 2018
Subtitle of host publicationProceedings of a meeting held 9-13 December 2018, Abu Dhabi, United Arab Emirates.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1506-1512
ISBN (Electronic)9781538649206
ISBN (Print)9781538649213
DOIs
Publication statusPublished - 19 Feb 2019
Event2018 IEEE Globecom Workshops, GC Wkshps 2018 - Abu Dhabi, United Arab Emirates
Duration: 9 Dec 201813 Dec 2018

Conference

Conference2018 IEEE Globecom Workshops, GC Wkshps 2018
CountryUnited Arab Emirates
CityAbu Dhabi
Period9/12/1813/12/18

Keywords

  • IoT networks
  • machine learning
  • measurement refinement
  • Wireless localization
  • wireless sensor networks

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