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Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks.

Kwangjae Sung1,2, Hyung Kyu Lee3, Hwangnam Kim4

  • 1Development Division, Korea Institute of Atmospheric Prediction Systems, Seoul 07071, Korea. kjsung80@korea.ac.kr.

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Summary
This summary is machine-generated.

This study introduces a new indoor pedestrian localization system using a mobile phone. It combines radio-frequency signal strength fingerprinting and dead reckoning with an improved particle filter for accurate and efficient positioning.

Keywords:
dead reckoningindoor positioningparticle filteringreceived signal strength (RSS) fingerprintingsensor fusion

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Indoor pedestrian positioning is challenging due to sensor errors from microelectromechanical systems (MEMS) and radio-frequency (RF) signal variations.
  • Existing methods often combine received signal strength (RSS) fingerprinting with dead reckoning (DR) using Bayes filters, but particle filters (PF) can be computationally intensive.

Purpose of the Study:

  • To develop a more computationally efficient and accurate indoor pedestrian localization scheme for mobile phones.
  • To address the limitations of traditional methods, including MEMS sensor bias and RF signal degradation.

Main Methods:

  • The proposed system integrates RSS fingerprinting and DR with a novel Double-Stacked Particle Filter (DSPF).
  • DSPF estimates user position by fusing noisy data from RSS and DR, utilizing proposal and target distributions for improved accuracy.
  • The algorithm is implemented on a mobile phone platform for practical indoor navigation.

Main Results:

  • The DSPF algorithm demonstrated superior localization accuracy compared to Kalman filtering-based methods.
  • It achieved competitive accuracy with standard PFs but with significantly higher computational efficiency.
  • Experimental results confirmed the DSPF's ability to provide reliable and accurate indoor positioning.

Conclusions:

  • The Double-Stacked Particle Filter (DSPF) offers an effective solution for accurate and computationally efficient indoor pedestrian localization.
  • This method enhances mobile phone-based navigation by overcoming the limitations of existing techniques.
  • The DSPF presents a promising advancement for real-world indoor positioning applications.