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A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting.

Óscar Belmonte-Fernández1, Emilio Sansano-Sansano1, Antonio Caballer-Miedes1

  • 1Institute of New Imaging Technologies, Jaume I University, 12071 Castelló de la Plana, Spain.

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This study introduces a new generative indoor localization method using Wi-Fi fingerprinting and hidden Markov models. It leverages temporal signal patterns for improved accuracy in mobile computing applications.

Keywords:
Wi-Fi fingerprintinghidden Markov modelsindoor localizationmachine learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Indoor localization is crucial for mobile computing, with Wi-Fi fingerprinting being a prevalent technique.
  • Existing Wi-Fi localization methods primarily use discriminative approaches.
  • There is a need for methods that can exploit temporal signal characteristics for enhanced localization accuracy.

Purpose of the Study:

  • To present a novel generative indoor localization method based on Wi-Fi fingerprinting.
  • To model Received Signal Strength Indicator (RSSI) using a hidden Markov model (HMM).
  • To utilize the temporal autocorrelation inherent in Wi-Fi signals for improved localization.

Main Methods:

  • A generative approach for indoor localization using Wi-Fi fingerprinting.
  • Modeling Wi-Fi Received Signal Strength Indicator (RSSI) with a hidden Markov model (HMM).
  • Employing the forward algorithm to calculate the likelihood of RSSI time series for location estimation.

Main Results:

  • The proposed HMM-based generative method achieved competitive results against four other Machine Learning algorithms in real-world scenarios.
  • The method demonstrated superior performance, ranking best in 17 out of 60 tested experiments.
  • The generative approach effectively utilizes temporal signal patterns, outperforming other methods in specific conditions.

Conclusions:

  • The generative HMM-based Wi-Fi fingerprinting method offers a promising alternative to discriminative approaches for indoor localization.
  • This technique effectively capitalizes on the temporal dynamics of Wi-Fi signals.
  • The proposed method shows significant potential for enhancing the accuracy and robustness of indoor positioning systems.