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RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems

Author

Listed:
  • Imran Moez Khan

    (College of Science, Technology, Engineering and Mathematics, RMIT University, Melbourne, VIC 3000, Australia)

  • Andrew Thompson

    (Robert Bosch Australia & New Zealand, Melbourne, VIC 3168, Australia)

  • Akram Al-Hourani

    (College of Science, Technology, Engineering and Mathematics, RMIT University, Melbourne, VIC 3000, Australia)

  • Kandeepan Sithamparanathan

    (College of Science, Technology, Engineering and Mathematics, RMIT University, Melbourne, VIC 3000, Australia)

  • Wayne S. T. Rowe

    (College of Science, Technology, Engineering and Mathematics, RMIT University, Melbourne, VIC 3000, Australia)

Abstract

Complementing RSSI measurements at anchors with onboard smartphone accelerometer measurements is a popular research direction to improve the accuracy of indoor localization systems. This can be performed at different levels; for example, many studies have used pedestrian dead reckoning (PDR) and a filtering method at the algorithm level for sensor fusion. In this study, a novel conceptual framework was developed and applied at the data level that first utilizes accelerometer measurements to classify the smartphone’s device pose and then combines this with RSSI measurements. The framework was explored using neural networks with room-scale experimental data obtained from a Bluetooth low-energy (BLE) setup. Consistent accuracy improvement was obtained for the output localization classes (zones), with an average overall accuracy improvement of 10.7 percentage points for the RSSI-and-device-pose framework over that of RSSI-only localization.

Suggested Citation

  • Imran Moez Khan & Andrew Thompson & Akram Al-Hourani & Kandeepan Sithamparanathan & Wayne S. T. Rowe, 2023. "RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems," Future Internet, MDPI, vol. 15(6), pages 1-17, June.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:220-:d:1175198
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    Cited by:

    1. Madiyar Nurgaliyev & Askhat Bolatbek & Batyrbek Zholamanov & Ahmet Saymbetov & Kymbat Kopbay & Evan Yershov & Sayat Orynbassar & Gulbakhar Dosymbetova & Ainur Kapparova & Nurzhigit Kuttybay & Nursulta, 2024. "Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method," Future Internet, MDPI, vol. 16(12), pages 1-28, December.

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