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Related Experiment Video

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Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.

Xiang He1, Daniel N Aloi2, Jia Li3

  • 1Department of Electrical and Computer Engineering, Oakland University, 2200 N Squirrel Road, Rochester, MI 48309, USA. xhe2@oakland.edu.

Sensors (Basel, Switzerland)
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Summary

This study introduces a new indoor positioning system for mobile devices using sensor fusion within a hidden Markov model (HMM). The system effectively combines data from multiple sensors for accurate user positioning.

Keywords:
HMM frameworkgraph structureiOS platformindoor positioningmultimodal particle filtersensor fusion

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Area of Science:

  • Mobile computing
  • Sensor fusion
  • Indoor positioning systems

Background:

  • Smart mobile devices are equipped with diverse sensors like accelerometers, gyroscopes, and WiFi signal indicators.
  • Existing indoor positioning techniques leverage these sensors but often face accuracy limitations.
  • Probabilistic sensor fusion offers a promising approach to enhance indoor localization accuracy.

Purpose of the Study:

  • To investigate the probabilistic fusion of multiple sensors within a hidden Markov model (HMM) framework for mobile-device user-positioning.
  • To develop an efficient indoor positioning system leveraging multimodal sensor data.
  • To evaluate the performance of the proposed system in a real-world indoor environment.

Main Methods:

  • Utilized a hidden Markov model (HMM) framework for probabilistic sensor fusion.
  • Proposed a graph structure for offline model training using multiple sensors.
  • Implemented a multimodal particle filter for seamless online sensor data fusion.
  • Developed an indoor positioning system on the iOS platform.

Main Results:

  • The proposed algorithm demonstrated promising results in experimental evaluations.
  • The system achieved accurate user positioning by effectively fusing data from various onboard sensors.
  • The graph-based model storage and particle filter fusion proved effective for real-time tracking.

Conclusions:

  • The developed indoor positioning system offers a robust solution for mobile devices.
  • Probabilistic sensor fusion within an HMM framework significantly enhances positioning accuracy.
  • The system design is suitable for practical deployment in typical indoor environments.