High precision indoor positioning method based on visible light communication using improved Camshift tracking algorithm

Mao, Wanchao; Xie, Hongyun; Tan, Zequn; Liu, Zhipeng; Liu, Manxi

doi:10.1016/j.optcom.2020.125599

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…e application of indoor navigation system will be very extensive, such as indoor logistics sorting system, library book inquiry and book management, underground parking lot parking space search and car demand, mine safety tracking, and so on. At present, almost all positioning technologies are affected by the environment; for example, the change of cloudy and rainy days and cloud thickness will change the positioning accuracy, and the occlusion of high-rise buildings on satellite signals will also reduce the positioning accuracy (Xie et al) [3]. In the indoor environment, changes in the environment may make the positioning system fail.…”

Section: Introductionmentioning

confidence: 99%

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

Zhang

Wang

et al. 2022

Mathematical Problems in Engineering

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With the development of modern society, the demand for indoor positioning technology is higher and higher. The existing indoor positioning technology is difficult to really solve the problem of accuracy and achieve high-performance indoor positioning system design. Based on iBeacon equipment, this paper proposes a method to optimize received signal strength indication indoor positioning algorithm by using the Gaussian filtering method so as to reduce the adverse impact of multipath fading in indoor environment. In order to further improve the accuracy of indoor positioning algorithm, the stack automatic encoder in the deep neural network algorithm is introduced. Through the deep learning method, the high-dimensional information of the fingerprint database collected by the system can be extracted and the adverse impact of data noise on the database is also reduced. Through the simulation test of the system, it can be seen that the error of received signal strength indication indoor positioning algorithm based on extended Gaussian filter is small. Compared with the traditional iBeacon algorithm, the improved algorithm can achieve better data classification. The maximum error of the whole system is 1.02 M. Comprehensive analysis shows that the proposed indoor positioning system has a certain practical value and can be applied to the indoor positioning needs in a certain range of environment.

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Section: Introductionmentioning

confidence: 99%

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

Zhang

Wang

et al. 2022

Mathematical Problems in Engineering

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“…A novel tracking algorithm that harnesses visible light communication has been introduced to further advance positioning systems' proficiency, elevating the standard for indoor positioning precision. This method demonstrates VLP's adaptability to intricate indoor landscapes and establishes new benchmarks for overcoming traditional navigation challenges in such environments [22]. The dynamic nature of indoor spaces poses significant challenges in decoding VLP signals.…”

Section: Related Workmentioning

confidence: 90%

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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“…For instance, the authors in [113] propose a two-phase indoor positioning algorithmic framework, namely coarse phase and fine phase when subject to insufficient anchor luminaries. In [114], continuously adaptive meanshift algorithm is performed to improve the robustness. Even if most of the LED is shielded or damaged, the system can still achieve high-precision tracking by utilizing color recognition and local detection, regardless of changes in shape.…”

Section: Robustnessmentioning

confidence: 99%

A Survey of Visible Light Communication Based Indoor Positioning Systems

Wang,

Niu,

Cao

et al. 2024

Preprint

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There is a growing demand for indoor positioning systems (IPS) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging: IPS based on visible light communication (VLC), which offers a combination of which is highly accurate, low cost and energy efficient. This article presents a comprehensive review of VLC-based IPS, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. The mechanisms of VLC-based IPS are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity and efficiency of VLC-based IPS is examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize the system, a variety of strategies are explored. These include the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the use of state-of-the-art localization algorithms. Additionally, system parameters such as LED placement, receiver sensitivity, and transmit power are carefully examined to highlight their impact on energy efficiency and localization accuracy. Altogether, this paper serves as a comprehensive guide to VLC IPS, providing in-depth insights into its vast potential and the challenges it presents.

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High precision indoor positioning method based on visible light communication using improved Camshift tracking algorithm

Cited by 14 publications

References 19 publications

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

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

A Survey of Visible Light Communication Based Indoor Positioning Systems

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