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Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light
Willem Raes1, Nicolas Knudde2, Jorik De Bruycker1
1ESAT-TELEMIC, KU Leuven, 9000 Ghent, Belgium.
Sensors (Basel, Switzerland)
|October 30, 2020
Summary
Machine learning models, including Multilayer Perceptron (MLP) and Gaussian processes (GP), show improved robustness for Received Signal Strength (RSS)-based Visible Light Positioning (VLP). Gaussian processes demonstrated superior performance in experimental evaluations.
Area of Science:
- Optical Wireless Communications
- Machine Learning Applications
- Indoor Positioning Systems
Background:
- Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is a promising technology for indoor navigation.
- Robustness to signal variations is a key challenge for VLP systems.
Purpose of the Study:
- To experimentally evaluate Machine Learning (ML) methods for robust RSS-based VLP.
- To compare the performance of Multilayer Perceptron (MLP) and Gaussian processes (GP) models.
Main Methods:
- Utilized light-emitting diodes (LEDs) and a single photodiode (PD) for RSS measurements.
- Investigated two relative RSS normalization schemes: maximum RSS normalization and RSS ratios.
- Collected datasets with modified LED power and simulated dust obstruction for robustness testing.
Main Results:
- MLP and GP models significantly outperformed traditional multilateration strategies in VLP accuracy and robustness.
- Gaussian processes exhibited greater robustness than MLP models under varying signal conditions.
- Both ML models demonstrated effectiveness in mitigating signal strength modifications.
Conclusions:
- ML-based approaches, particularly Gaussian processes, offer a robust solution for RSS-based VLP.
- The findings highlight the potential of ML to enhance the reliability of visible light positioning systems.
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