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  • 1Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia. liy19@student.unimelb.edu.au.

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Summary

This study introduces a Wi-Fi fingerprinting system using a hidden Markov model for accurate indoor location tracking. The novel approach achieves 97% room-level accuracy in a real university Wi-Fi network.

Keywords:
Expectation-Maximisation imputationHidden Markov Model (HMM)Multivariate Gaussian Mixture Model (MVGMM)Wi-Fi localisationmultivariate linear regression

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Indoor localization is crucial for mobile user tracking.
  • Existing Wi-Fi based methods face challenges like high-dimensional data, device heterogeneity, and missing access point data.
  • Accurate room-level awareness is essential for many location-based services.

Purpose of the Study:

  • To develop a robust and accurate indoor localization system using Wi-Fi fingerprinting.
  • To address the challenges of high-dimensional data, heterogeneous devices, and missing data in Wi-Fi signal strength measurements.
  • To improve mobile user tracking in real-world public environments.

Main Methods:

  • A probabilistic Wi-Fi fingerprinting method within a hidden Markov model (HMM) framework was employed.
  • A Multivariate Gaussian Mixture Model (MVGMM) was used to model the spatial correlation of Received Signal Strength (RSS) measurements.
  • The study investigated the utility of 'unseen' or invisible access points as differentiating information.

Main Results:

  • The proposed system demonstrates comparable localization performance to existing methods.
  • Field tests in a university campus Wi-Fi network achieved a reliable 97% room-level localization accuracy.
  • The method effectively handles high-dimensional data and missing RSS measurements.

Conclusions:

  • The hidden Markov model-based Wi-Fi fingerprinting approach offers an accurate and reliable solution for indoor localization.
  • The system successfully addresses key challenges in real-world Wi-Fi localization, including device heterogeneity and data incompleteness.
  • The utilization of invisible access points proved beneficial for cell differentiation and overall localization accuracy.