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Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication.

Itziar Alonso-González^1,2, David Sánchez-Rodríguez^3,4, Carlos Ley-Bosch^5,6

¹Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain. mangel.quintana@ulpgc.es.

Sensors (Basel, Switzerland)

|March 31, 2018

Summary

This study introduces a novel indoor positioning system using Visible Light Communication (VLC) and neural networks. The system achieves over 99% accuracy for precise 3D localization, outperforming traditional radiofrequency methods.

Keywords:

indoor localization neural network received signal strength visible light communication

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Area of Science:

Electrical Engineering
Computer Science
Signal Processing

Background:

Indoor localization is crucial for location-aware services.
Radiofrequency-based methods suffer from multipath propagation and limited accuracy (up to 2 meters).
Visible Light Communication (VLC) offers a promising alternative due to stable optical power and immunity to electromagnetic interference.

Purpose of the Study:

To propose a high-accuracy indoor positioning estimation system using Visible Light Communication (VLC).
To leverage neural networks for precise 3D localization based on received signal strength fingerprinting.
To enhance indoor positioning accuracy beyond traditional radiofrequency methods.

Main Methods:

A fingerprinting indoor positioning system was developed using a dataset of received signal strength from a grid of points.
Three neural networks were employed, each dedicated to estimating a specific Cartesian coordinate (x, y, z).
The system predicts device position in a 3D environment based on signal strength data.

Main Results:

The proposed system demonstrates substantial accuracy improvements over traditional fingerprinting methods.
Achieved an accuracy exceeding 99% for indoor localization.
Reported an average error distance of 0.4 mm.

Conclusions:

Visible Light Communication (VLC) combined with neural network-based fingerprinting provides a highly accurate indoor positioning solution.
The proposed system overcomes limitations of radiofrequency methods, offering centimeter-level or better precision.
This approach paves the way for advanced location-aware services requiring precise indoor navigation.