Wi-Fi Indoor Localization based on Multi-Task Deep Learning

Wei Yuan Lin, Ching-Chun Huang, Nguyen Tran Duc, Hung Nguyen Manh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Conventional Wi-Fi-based indoor localization methods rely on training a RSS fingerprint model to predict user locations. Most fingerprinting models only consider the distribution of RSS (radio signal strength) at a location and ignore the relationship between adjacent locations. Another challenging issue is the RSS inconsistency problem where the RSSs of neighboring locations are not as similar as the ideal expectation. To address these problems, we suggest well utilizing the richer regional features rather than the raw RSSs. Thereby, we proposed a deep learning network which integrates three components: the One-Dimension-Convolutional Neural Network to extract regional RSS features, the Siamese architecture to handle the similarity inconsistency problem, and the Regression network for user positioning. Our experiments present promising results compared with the state-of-art methods.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
DOIs
StatePublished - 31 Jan 2019
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
CountryChina
CityShanghai
Period19/11/1821/11/18

Keywords

  • Convolutional Neural Network
  • indoor localization
  • radio Fingerprint
  • regression Network
  • siamese Network

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