Unsupervised radio map learning for indoor localization

Ching-Chun Huang, Wei Chi Chan, Manh Hung-Nguyen

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

2 Scopus citations

Abstract

For radio-based indoor localization, the approaches founded on the radio fingerprint concept are efficient duo to low cost and the ability to handle occlusion effects. However, the approaches require a lot of human labor to label training data for radio map (fingerprint) construction. To address this issue, in this paper, we proposed an unsupervised framework to learn a Wi-Fi radio map in an indoor environment. Unlike conventional approaches that depend on a simulated radio map or a prior radio propagation model to reduce human efforts, our method uses Wi-Fi and IMU signals collecting by crowdsourcing to build a robust radio map automatically. More concretely, four types of constraints are fused by the proposed radio map optimization procedure. They include the alignment of Wi-Fi landmarks, the displacement constraint, the manifold-based smooth constraint, and the inter-trajectory constraints. Our experiment results also show the effectiveness of the unsupervised radio map.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages79-80
Number of pages2
ISBN (Electronic)9781509040179
DOIs
StatePublished - 25 Jul 2017
Event4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017 - Taipei, United States
Duration: 12 Jun 201714 Jun 2017

Publication series

Name2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017

Conference

Conference4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
CountryUnited States
CityTaipei
Period12/06/1714/06/17

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