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Summary
This summary is machine-generated.

This study introduces an AI-driven approach for indoor visible light positioning (VLP) systems. The method enhances accuracy by estimating receiver height and classifying room areas to mitigate multipath interference, achieving centimeter-level precision.

Keywords:
artificial intelligence algorithmsindoor positioningpoint classificationvisible light communication

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Robotics

Background:

  • Received Signal Strength Indication (RSSI)-based indoor visible light positioning (VLP) systems traditionally require known receiver height for distance calculation.
  • Positioning accuracy in VLP is significantly degraded by multipath interference, which varies spatially within a room.
  • Standard positioning methods exhibit sharp increases in error at room edges when only RSSI is utilized.

Purpose of the Study:

  • To develop an enhanced RSSI-based VLP scheme that overcomes limitations of existing 2D and 3D positioning methods.
  • To improve indoor positioning accuracy by addressing multipath effects and edge-area errors.
  • To extend traditional RSSI trilateral positioning to 3D using AI-driven height estimation and point classification.

Main Methods:

  • Implemented an artificial intelligence (AI) algorithm for point classification to categorize location points into ordinary, edge, and blind areas.
  • Developed a height estimation method based on received power data from different Light Emitting Diodes (LEDs) to enable 3D positioning.
  • Applied distinct processing models for each classified point type to mitigate multipath interference.
  • Integrated processed data into a trilateral positioning algorithm to calculate coordinates and reduce edge-corner errors.

Main Results:

  • The proposed scheme successfully extends RSSI trilateral positioning from 2D to 3D by incorporating height estimation.
  • AI-based point classification effectively reduces the impact of multipath effects, particularly in challenging edge and blind zones.
  • The system demonstrates a significant reduction in average indoor positioning error, especially in room corners.
  • Experimental simulations validated the proposed methods, achieving centimeter-level positioning accuracy.

Conclusions:

  • The novel AI-driven VLP scheme significantly enhances indoor positioning accuracy and robustness.
  • By intelligently handling different room areas and estimating height, the system overcomes key limitations of traditional RSSI-based VLP.
  • The achieved centimeter-level accuracy positions this method as a promising solution for precise indoor navigation and localization applications.