A Convolutional Neural Network Architecture for Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

Xin Chen, Wan Chi Siu, Yuk Hee Chan, Chuen Yu Chan, Chun Pong Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Nowadays Location Based Services applications are increasingly useful. However, problems like floor identification for multi-buildings and adverse effects of devices diversity are needed to be resolved. In this paper we propose a new approach using cosine similarity computed by Wi-Fi fingerprints and radio map and using Convolutional Neural Network (CNN) model to achieve multi-floor classification. We propose in this paper to use locations-based similarity as the feature vector instead of using conventional Access Point sets. We also use a timesaving walk-survey method to collect Wi-Fi fingerprint. Experimental results show that our proposed CNN floor classifier has 98.37% training accuracy and 99.51% test accuracy. Compared with recent deep neural networks, our proposed approach achieves state-of-the-art floor classification accuracy but only needs a training data set almost 5 times smaller than that of other approaches.

Original languageEnglish
Title of host publication2023 24th International Conference on Digital Signal Processing, DSP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9798350339598
DOIs
Publication statusPublished - Jun 2023
Event24th International Conference on Digital Signal Processing, DSP 2023 - Rhodes, Greece
Duration: 11 Jun 202313 Jun 2023

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2023-June

Conference

Conference24th International Conference on Digital Signal Processing, DSP 2023
Country/TerritoryGreece
CityRhodes
Period11/06/2313/06/23

Keywords

  • CNN
  • Cosine similarity
  • floor classification
  • small data set

ASJC Scopus subject areas

  • Signal Processing

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