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A floor-map-aided WiFi/pseudo-odometry integration algorithm for an indoor positioning system.

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This study presents a novel indoor positioning system fusing floor maps, WiFi, and smartphone sensor data. The system achieves reliable meter-level positioning accuracy without requiring new infrastructure.

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Area of Science:

  • Indoor positioning systems
  • Sensor fusion
  • Geomatics engineering

Background:

  • Accurate indoor positioning is crucial for various applications.
  • Existing methods often require dedicated infrastructure or suffer from low accuracy.
  • Integrating diverse data sources offers a promising approach to overcome these limitations.

Purpose of the Study:

  • To develop an infrastructure-free indoor positioning scheme.
  • To achieve meter-level positioning accuracy by fusing multiple data sources.
  • To address common challenges in indoor localization such as "go and back" and "cross-wall" phenomena.

Main Methods:

  • Utilizing a topology-constrained K nearest neighbor (KNN) algorithm with floor map data.
  • Simulating pseudo-odometry (P-O) using pedestrian dead reckoning (PDR) and integrating WiFi data.
  • Employing an adaptive fading-factor-based extended Kalman filter (EKF) to fuse WiFi, P-O, and heading data.
  • Implementing a floor-map-aided particle filter to mitigate gross errors and solve the "cross-wall" problem.

Main Results:

  • The proposed scheme successfully fuses floor map, WiFi, and smartphone sensor data.
  • Meter-level positioning accuracy was reliably achieved in experimental validation.
  • The system effectively eliminates the "go and back" and "cross-wall" positioning errors.

Conclusions:

  • The developed fusion scheme provides a robust and accurate solution for indoor positioning.
  • The integration of floor maps with WiFi and sensor data significantly enhances localization performance.
  • This approach offers a practical and infrastructure-free alternative for precise indoor navigation.