使用单个 LED 发射器进行 IRS 辅助可见光定位

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-10-10 DOI:10.1016/j.dsp.2024.104799
Efe Tarhan , Furkan Kokdogan , Sinan Gezici
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引用次数: 0

摘要

我们提出了一种可见光定位(VLP)系统,该系统由一个发光二极管(LED)发射器和一个智能反射面(IRS)组成,用于估计装有单个光电探测器的接收器的位置。通过 LED 发射器进行多次发射,并优化每次发射的 IRS 元件方向向量,接收器可根据 IRS 反射信号的功率测量值提取位置信息。针对提议的设置,提出了理论极限和最大似然 (ML) 估计器。此外,还提出了一种名为 IRS 聚焦的算法,用于在定位过程中确定 IRS 元件的方向。通过模拟演示了所提出的定位方法的有效性。此外,还提供了扩展功能,以便在接收器位置存在部分先验信息以及 IRS 位于 LED 发射器时应用所提出的方法。
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IRS aided visible light positioning with a single LED transmitter
We propose a visible light positioning (VLP) system with a single light emitting diode (LED) transmitter and an intelligent reflecting surface (IRS) for estimating the position of a receiver equipped with a single photo-detector. By performing a number of transmissions from the LED transmitter and optimizing the orientation vectors of the IRS elements for each transmission, position information is extracted by the receiver based on power measurements of the signals reflecting from the IRS. The theoretical limit and the maximum likelihood (ML) estimator are presented for the proposed setting. In addition, an algorithm, named IRS focusing, is proposed for determining the orientations of the IRS elements during the localization process. The effectiveness of the proposed localization approach is demonstrated through simulations. Furthermore, extensions are provided to apply the proposed approach in the presence of partial prior information about the receiver position and when the IRS is located at the LED transmitter.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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