Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning

Yu Ting Liu, Jen-Jee Chen, Yu-Chee Tseng, Frank Y. Li

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

8 Scopus citations

Abstract

Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence of latent codes are injected into an LSTM network to identify location. To assess the accuracy and effectiveness of our model, we perform extensive real-life experiments.

Original languageEnglish
Title of host publication30th International Conference on Computer Communications and Networks, ICCCN 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738113302
DOIs
StatePublished - 19 Jul 2021
Event30th International Conference on Computer Communications and Networks, ICCCN 2021 - Virtual, Athens, Greece
Duration: 19 Jul 202122 Jul 2021

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
Volume2021-July
ISSN (Print)1095-2055

Conference

Conference30th International Conference on Computer Communications and Networks, ICCCN 2021
Country/TerritoryGreece
CityVirtual, Athens
Period19/07/2122/07/21

Keywords

  • Auto-encoder
  • fingerprint positioning
  • indoor and outdoor environments
  • LSTM
  • WiFi RSSI

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