DNN-based Indoor Fingerprinting Localization with WiFi FTM

Paulson Eberechukwu, Hyunwoo Park, Christos Laoudias, Seppo Horsmanheimo, Sunwoo Kim

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

Abstract

In this work, we present a deep neural network (DNN)-based indoor fingerprinting localization method with WiFi fine time measurements (FTM). The proposed method leverages the WiFi FTM and its variance as environment features to provide accurate location estimation. An $i$-th layer DNN structure used in this paper is implemented by back propagation using an Adam optimizer. The weights and the bias of the $l-\text{th}$ layer that minimize the loss function is computed in order to minimize the positioning mean squared error (MSE). Experimental results using real-world data obtained in a typical office setting proves the efficiency of the proposed solution. The performance of the system is remarkably improved, using the $600\times 600$ hidden layer size of the DNN, we achieved an average positioning accuracy of 0.7 m and 0.9 m for the 68-th percentiles $(1-\sigma)$ and 95-th percentiles $(2-\sigma)$ respectively.
Original languageEnglish
Title of host publicationProceedings - 2022 23rd IEEE International Conference on Mobile Data Management, MDM 2022
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages367-371
Number of pages5
ISBN (Electronic)978-1-6654-5176-5
ISBN (Print)978-1-6654-5177-2
DOIs
Publication statusPublished - 9 Jun 2022
MoE publication typeA4 Article in a conference publication
Event2022 23rd IEEE International Conference on Mobile Data Management (MDM) - Paphos, Cyprus
Duration: 6 Jun 20229 Jun 2022

Conference

Conference2022 23rd IEEE International Conference on Mobile Data Management (MDM)
Period6/06/229/06/22

Keywords

  • Location awareness
  • Deep learning
  • Backpropagation
  • Neural networks
  • Estimation
  • Fingerprint recognition
  • Time measurement
  • Fingerprinting
  • Indoor localization
  • Deep Learning
  • FTM

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