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WiFi Based Fingerprinting Positioning Based on Seq2seq Model.

Haotai Sun1, Xiaodong Zhu1, Yuanning Liu1

  • 1School of Computer Science and Technology, Jilin University, Changchun 130012, China.

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|July 9, 2020
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Summary

This study introduces a deep learning approach for WiFi fingerprinting to improve indoor positioning. The seq2seq model leverages context information from WiFi signal sequences for more accurate location estimation in GPS-denied environments.

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WiFi based positioningdeep learningseq2seq modeltrajectory

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Indoor positioning is crucial in areas lacking GPS signals.
  • WiFi-based systems offer a practical solution due to widespread Access Point (AP) deployment.
  • Existing methods may not fully exploit contextual data within WiFi signal sequences.

Purpose of the Study:

  • To propose a novel deep learning method for WiFi fingerprinting using a seq2seq model.
  • To enhance indoor positioning accuracy by utilizing sequential context information.
  • To demonstrate the effectiveness of the proposed method against existing deep learning approaches.

Main Methods:

  • A seq2seq deep learning model was developed for WiFi fingerprinting.
  • The model learns from variable-length training sequences to capture context.
  • Contextual information, such as movement patterns, is exploited for improved positioning.

Main Results:

  • The proposed seq2seq model demonstrated improved performance on an open-source dataset.
  • The method showed superior accuracy compared to other deep learning-based indoor positioning techniques.
  • Exploiting context information within RSS fingerprints led to better localization.

Conclusions:

  • The seq2seq model offers a promising advancement for WiFi-based indoor positioning.
  • Leveraging context in WiFi signal sequences enhances location accuracy.
  • This deep learning approach is suitable for practical indoor positioning applications.