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Related Experiment Video

Updated: Jan 10, 2026

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

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Enhanced optical visible light positioning via RSS-based evolutionary optimization under LOS and NLOS conditions.

Pankaj Pathak¹, S Sharavanan², M Karthikeyan³

¹Symbiosis Institute of Digital and Telecom Management (SIDTM), Symbiosis International (Deemed University), Pune, India. pankajpathak@sidtm.edu.in.

Scientific Reports

|November 26, 2025

Summary

This summary is machine-generated.

This study enhances visible light positioning accuracy by using genetic and particle swarm optimization algorithms to minimize errors. Results show these methods significantly reduce positioning inaccuracies compared to traditional techniques.

Keywords:

Genetic algorithm Least squares estimation Particle swarm algorithm Positioning Wireless optical communication

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Area of Science:

Optical communication
Wireless positioning systems
Indoor navigation

Background:

Visible light positioning (VLP) offers a low-cost, safe alternative to RF-based positioning.
Existing VLP methods using Received Signal Strength (RSS) algorithms suffer significant errors due to noise and multipath effects.
Linear approximation in RSS-based VLP introduces further inaccuracies.

Purpose of the Study:

To investigate and reduce positioning errors in VLP systems.
To compare the effectiveness of evolutionary algorithms against traditional least squares estimation for error minimization.
To analyze the impact of various parameters on VLP accuracy.

Main Methods:

Utilized the Received Signal Strength (RSS) algorithm for locating within a simulated 6x6x4 m³ room.
Implemented genetic algorithm (GA) and particle swarm optimization (PSO) to minimize positioning error functions.
Simulated scenarios including Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, and analyzed effects of LED distance, transmitted power, receiver height, and reflections.

Main Results:

GA and PSO significantly reduced positioning errors compared to least squares estimation.
Increased transmitted power and number of transmitters decreased positioning error.
Higher receiver height and presence of reflections increased positioning error.
Achieved Root Mean Square Errors (RMSE) of 0.3493 m (LOS) and 0.4461 m (NLOS).

Conclusions:

Evolutionary algorithms like GA and PSO are effective in enhancing VLP accuracy.
Optimizing LED placement, transmitted power, and minimizing reflections are crucial for improving VLP performance.
A method for selecting high-power LEDs was proposed to further reduce positioning errors.