Precision indoor three‐dimensional visible light positioning using receiver diversity and multi‐layer perceptron neural network

Mahmoud, Abdulrahman A.; Ahmad, Zahir; Haas, Olivier; Rajbhandari, Sujan

doi:10.1049/iet-opt.2020.0046

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…The single FNN approach might be more straightforward in terms of hyper-parameter tuning, but the cellular approach is more accurate, improves scalability, and lessens the chance of overfitting. The authors in [78] apply a supervised feedforward BP FNN model to provide an accurate 3D estimate for the location of a PD sensor, utilizing RSS fingerprints. In this study, multi-path propagation is considered, and utilizing receiver diversity (i.e.…”

Section: ) DL Methodsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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Section: ) DL Methodsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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“…The following algorithms have featured for VLP [366]: vision analysis (based on projected images), proximity (also centroid), hyperbolic (TDOA) or circular (TOA/RSS) (non)linear least squares multilateration or multiangulation [13], [250], [421], [422], probabilistic or deterministic scene analysis or fingerprinting [423], explicit (Bayesian) statistics [330], [424] and iterative minimisation or maximum likelihood estimation [200], implicit (supervised) machine learning including deep learning-based classification (e.g. with K-nearest neighbour [425]) or regression [426]- [431]. These algorithms differ in their attainable QPoS, differing in terms of accuracy, latency, spatial dimension, noise sensitivity, complexity, robustness again multipath or power fluctuations, etc.…”

Section: B Localisation Algorithmsmentioning

confidence: 99%

Visible Light Positioning as a Next-Generation Indoor Positioning Technology: A Tutorial

Bastiaens,

Alijani,

Joseph

et al. 2024

IEEE Commun. Surv. Tutorials

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Indoor positioning technology has become undisputedly pervasive in modern society. It can be used for numerous applications in various environments, ranging from agriculture to smart industry. Among the different indoor positioning technologies, the development of the indoor visible light positioning (VLP) system has gained momentum recently thanks to its low cost and centimetre-order accuracy. Moreover, VLP is a promising candidate technology for integration with Light Fidelity (Li-Fi), which relies on the optical spectrum to build future wireless networks and plays a significant role in sixth-generation (6G) wireless networks. This tutorial investigates VLP as a novel positioning technology in depth. The inherent characteristics and fundamentals of this technology are extensively described with the components of a VLP system and possible localization approaches being discussed. Furthermore, a mathematical and technical discussion of a simple minimum retrofit of the single photodiode (PD) received signal strength (RSS) VLP systems is provided. Finally, the paper concludes with open challenges and a future roadmap for VLP.

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“…Advances in accuracy for location systems have been propelled by enhanced receiver diversity and advanced computational methods, such as neural networks, instrumental for achieving precise three-dimensional positioning. These breakthroughs signal a critical shift towards more robust and nuanced indoor navigation solutions [21].…”

Section: Related Workmentioning

confidence: 99%

Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case

Rufo,

Aguiar-Castillo,

Rufo

et al. 2024

Electronics

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This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements.

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Precision indoor three‐dimensional visible light positioning using receiver diversity and multi‐layer perceptron neural network

Cited by 11 publications

References 18 publications

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Visible Light Positioning as a Next-Generation Indoor Positioning Technology: A Tutorial

Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case

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