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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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3D Visible Light-Based Indoor Positioning System Using Two-Stage Neural Network (TSNN) and Received Intensity

Li-Sheng Hsu1,2, Chi-Wai Chow1,2, Yang Liu3

  • 1Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

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|November 26, 2022
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Summary
This summary is machine-generated.

This study introduces a novel two-stage neural network (TSNN) with a Received-Intensity-Selective-Enhancement (RISE) scheme for high-precision 3D visible light-based indoor positioning (VLIP). The system significantly reduces positioning errors caused by light non-overlap zones.

Keywords:
light emitting diode (LED)machine learningoptical wireless communication (OWC)visible light communication (VLC)visible light positioning (VLP)

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Indoor Positioning Systems

Background:

  • Visible Light-based Indoor Positioning (VLIP) systems offer high precision for navigation and tracking.
  • Increasing distances between light emitters and receivers degrade VLIP performance due to field-of-view limitations and non-overlap zones.
  • These non-overlap zones introduce significant positioning errors, especially in three-dimensional (3D) applications.

Purpose of the Study:

  • To present the first demonstration of a 3D VLIP system using a two-stage neural network (TSNN) model.
  • To introduce and validate the Received-Intensity-Selective-Enhancement (RISE) scheme to mitigate positioning errors.
  • To improve the accuracy and reliability of 3D VLIP systems.

Main Methods:

  • Development and implementation of a two-stage neural network (TSNN) model for 3D VLIP.
  • Integration of the Received-Intensity-Selective-Enhancement (RISE) scheme to address light non-overlap zones.
  • Experimental validation in a 200 × 150 × 300 cm³ test room.

Main Results:

  • The TSNN model with RISE reduced mean errors by 54.1% (z-direction) and 39.1% (xy-direction) in training data compared to a single-stage NN.
  • Testing data showed error reductions of 27.9% (z-direction) and 37.8% (xy-direction) with the TSNN-RISE system.
  • At P90 (CDF), the TSNN model with RISE achieved a 36.78% error reduction compared to the single-stage NN model.

Conclusions:

  • The proposed TSNN model combined with the RISE scheme effectively alleviates positioning errors in 3D VLIP systems.
  • This approach significantly enhances the accuracy of indoor positioning, particularly in challenging scenarios with light non-overlap.
  • The findings demonstrate a substantial improvement in VLIP performance for applications like robot navigation and access tracking.