Positioning Using Visible Light Communications: A Perspective Arcs Approach

Zhu, Zhiyu; Guo, Caili; Bao, Rongzhen; Chen, Mingzhe; Saad, Walid; Yang, Yang

doi:10.1109/twc.2023.3247458

Cited by 20 publications

(4 citation statements)

References 38 publications

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“…Visible Light Positioning (VLP) [12][13][14][15], utilizing LED lighting for precise data transmission and localization, represents another frontier in indoor navigation technologies. Recent studies have focused on improving the integration of optical localization techniques with network infrastructures, enhancing mobility awareness, and refining channel modeling.…”

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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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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“…Visible light communications (VLC) have emerged as a significant technology for overcoming these challenges by employing visible light for both data transmission and accurate positioning [2]. Offering advantages over conventional technologies like GPS, radar, and lidar, VLC stands out for its data security, cost-effectiveness, and practicality [3].…”

Section: Introductionmentioning

confidence: 99%

Vehicular Visible Light Positioning System Based on a PSD Detector

Raissouni,

De-La-Llana-Calvo,

Lázaro-Galilea

et al. 2024

Sensors

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In this paper, we explore the use of visible light positioning (VLP) technology in vehicles in intelligent transportation systems (ITS), highlighting its potential for maintaining effective line of sight (LOS) and providing high-accuracy positioning between vehicles. The proposed system (V2V-VLP) is based on a position-sensitive detector (PSD) and exploiting car taillights to determine the position and inter-vehicular distance by angle of arrival (AoA) measurements. The integration of the PSD sensor in vehicles promises exceptional positioning accuracy, opening new prospects for navigation and driving safety. The results revealed that the proposed system enables precise measurement of position and distance between vehicles, including lateral distance. We evaluated the impact of different focal lengths on the system performance, achieving cm-level accuracy for distances up to 35 m, with an optimum focal length of 25 mm, and under low signal-to-noise conditions, which meets the standards required for safe and reliable V2V applications. Several experimental tests were carried out to validate the results of the simulations.

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“…Signals of opportunity refer to existing nonnavigation radio frequency (RF) signals in the electromagnetic environment. Magnetic signals [8], analog modulated broadcast signals [9], Digital Terrestrial Multimedia Broadcast (DTMB) signals [10], Bluetooth signals [11], UWB signals [12], WiFi signals [13], mobile communication signals [14], visible light signals [15] can all be used for NAVSOP. Among them, WiFi signals have gained widespread attention in indoor positioning applications due to advantages such as the easy knowledge of the router's location and wide indoor distribution [16].…”

Section: Introductionmentioning

confidence: 99%

FM-Based Positioning via Deep Learning

Zheng,

Hu,

Zhang

et al. 2023

Preprint

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<p>Frequency modulation (FM) broadcast signals, as opportunity signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on received signal strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce an end-to-end FM-based positioning method that leverages deep learning, known as FM-Pnet. This method utilizes the time-frequency representation of FM signals as the network input, allowing the network to automatically learn deep features for positioning. We further propose two strategies, noise injection and enriching training samples, to enhance the model's generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.</p>

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Positioning Using Visible Light Communications: A Perspective Arcs Approach

Cited by 20 publications

References 38 publications

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

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

Vehicular Visible Light Positioning System Based on a PSD Detector

FM-Based Positioning via Deep Learning

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