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Robust Design and Experimental Demonstration of RSS-Based Visible Light Positioning Under Limited Receiving FOV

IEEE Transactions on Green Communications and Networking
In this paper, we propose a robust received signal strength (RSS)-based indoor visible light positioning (VLP) scheme under limited receiving field-of-view (FOV). The position estimation in the time domain is complicated, and the time-frequency transform
Nuo Huang   +5 more
semanticscholar   +1 more source

Enhancing RSS-Based Visible Light Positioning by Optimal Calibration of LED Tilt and Gain

IEEE Transactions on Communications
This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often-overlooked impact of light-emitting diode ...
Fan Wu   +3 more
semanticscholar   +1 more source

DeepVLP: A Graph Neural Network-Based Denoising and Signals Optimization Framework for Visible Light Positioning

IEEE Transactions on Mobile Computing
Visible Light Positioning (VLP) has emerged as a promising technique in the Internet of Things landscape and gained increasing attention worldwide due to its widely existing infrastructure, high precision, and cost-effectiveness.
Xiansheng Yang   +5 more
semanticscholar   +1 more source

Optical IRS Assisted-Visible Light Positioning in Indoor Non-LOS IoV Scenarios

IEEE Internet of Things Journal
The demand for high-precision localization services has surged significantly due to the rise of intelligent transportation and autonomous driving in large-scale indoor factories.
Yida Guo   +5 more
semanticscholar   +1 more source

Lightweight RSS-Based Visible Light Positioning Considering Dust Accumulation

Journal of Lightwave Technology
The performance of received signal strength (RSS)-based visible light positioning (VLP) is heavily reliant on the pre-existing channel model which describes the RSS attenuation with distance.
Ling Jin   +7 more
semanticscholar   +1 more source

Interference Mitigation in Visible Light Positioning Using Compressed Sensing and Machine Learning

IEEE Internet of Things Journal
Visible light positioning (VLP) systems have shown significant promise for indoor localization, providing advantages, such as environmental friendliness, immunity to electromagnetic interference, and enhanced security.
Xianbei Meng, Man Feng, Haibo Ma
semanticscholar   +1 more source

Optical Intelligent Reflecting Surface-Aided Visible Light Positioning Based on RSS Fingerprint

ICC 2025 - IEEE International Conference on Communications
In this paper, an optical intelligent reflecting surface (OIRS)-aided visible light positioning (VLP) system that adopt received signal strength (RSS) fingerprint method is proposed to enhance the positioning accuracy.
Qi Zhang   +4 more
semanticscholar   +1 more source

High‐Precision Indoor Visible Light Positioning Method for Line‐Of‐Sight Scenes Based on a Spatiotemporal Sequence Attention Mechanism

Concurrency and Computation
Using deep learning to improve the accuracy of indoor visible light positioning (VLP) systems has gradually become a widely used research strategy in the field.
Yonghao Yu   +4 more
semanticscholar   +1 more source

Single LED visible light positioning scheme based on intelligent reflecting surfaces.

Applied Optics
Visible light positioning (VLP) systems have gained attention for their ability to operate without additional infrastructure. However, most positioning schemes rely on multiple LEDs, which presents a considerable challenge in scenarios where only a ...
Jin Shi   +5 more
semanticscholar   +1 more source

Indoor Visible Light Positioning Using a Novel Fingerprint Regeneration Method Under Uneven Fingerprint Distribution Scenarios

International Conference on Communication, Computing & Security
Traditional fingerprint-based visible light positioning (VLP) methods require the collection of received signal strength (RSS) values from a large number of reference locations.
Jinzhe Wang   +4 more
semanticscholar   +1 more source

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