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通过在LOS和NLOS条件下的基于RSS的进化优化来增强光学可见光定位

Pankaj Pathak¹, S Sharavanan², M Karthikeyan³

¹Symbiosis Institute of Digital and Telecom Management (SIDTM), Symbiosis International (Deemed University), Pune, India. pankajpathak@sidtm.edu.in.

Scientific reports

|November 26, 2025

概括

这项研究通过使用遗传和粒子群优化算法来提高可见光定位准确度,以尽量减少错误. 结果显示,与传统技术相比,这些方法显著减少了定位不准确性.

科学领域:

光学通信是一种光学通信.
无线定位系统是无线定位系统.
室内导航室内导航

背景情况:

可见光定位 (VLP) 为基于射频的定位提供了一个低成本,安全的替代方案.
使用收到信号强度 (RSS) 算法的现有 VLP 方法由于噪声和多路径效应而遭受重大错误.
基于RSS的VLP中的线性近似引入了进一步的不准确性.

研究的目的:

调查和减少VLP系统中的定位错误.
为了将进化算法的有效性与传统最小平方估计进行比较,以尽量减少错误.
分析各种参数对VLP准确性的影响.

主要方法:

使用接收信号强度 (RSS) 算法定位在模拟的6x6x4立方米房间内.
实现了基因算法 (GA) 和粒子群优化 (PSO) 以尽量减少定位错误函数.
模拟场景包括视线 (LOS) 和非视线 (NLOS) 条件,并分析了LED距离,传输功率,接收器高度和反射的影响.

主要成果:

与最小平方估计相比,GA和PSO显著减少了定位错误.
传输功率的增加和发射器的数量减少了定位错误.
更高的接收器高度和反射的存在增加了定位错误.
实现了0.3493米 (LOS) 和0.4461米 (NLOS) 的根平均平方误差 (RMSE).

结论:

像GA和PSO这样的进化算法在提高VLP准确性方面是有效的.
优化LED放置,传输功率和最小化反射对于提高VLP性能至关重要.
提出了一种选择高功率LED的方法,以进一步减少定位错误.

关键词:

遗传算法遗传算法遗传算法最小平方估计的估计.粒子群算法粒子群算法定位定位定位定位定位定位无线光学通信无线光学通信

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