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室内可见光定位系统基于点分类,使用人工智能算法.

Qianqian Long1, Junyi Zhang1, Lu Cao1

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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概括
此摘要是机器生成的。

这项研究介绍了人工智能驱动的室内可见光定位 (VLP) 系统的方法. 该方法通过估计接收器高度和分类房间区域来减轻多路径干扰来提高准确性,从而达到厘米级的精度.

关键词:
人工智能算法的人工智能算法室内定位装置 室内定位装置积分分类是指分类点的分类.可见光通信可见光通信

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 基于接收信号强度指示 (RSSI) 的室内可见光定位 (VLP) 系统传统上需要已知的接收器高度来计算距离.
  • 在VLP中定位精度因多路干扰而显著降低,多路干扰在一个房间内空间不同.
  • 当仅使用RSSI时,标准定位方法会在房间边缘显著增加误差.

研究的目的:

  • 开发基于RSSI的增强型VLP方案,克服现有的2D和3D定位方法的局限性.
  • 通过解决多路径效应和边缘区域错误来提高室内定位精度.
  • 将传统的RSSI三边定位扩展到3D,使用人工智能驱动的高度估计和点分类.

主要方法:

  • 实现了人工智能 (AI) 算法用于点分类,以将位置点分类为普通,边缘和盲区.
  • 开发了一种基于不同发光二极管 (LED) 接收的功率数据的高度估计方法,以实现3D定位.
  • 为每个分类点类型应用不同的处理模型,以减轻多路径干扰.
  • 将处理的数据集成到三边定位算法中,以计算坐标并减少边缘角误差.

主要成果:

  • 拟议的方案成功地将RSSI三边定位从2D扩展到3D,通过结合高度估计.
  • 基于人工智能的点分类有效地减少了多路径效应的影响,特别是在具有挑战性的边缘和盲区.
  • 该系统显著减少了室内平均定位误差,特别是在房间的角落.
  • 实验模拟验证了拟议的方法,达到厘米级定位准确度.

结论:

  • 这种新的AI驱动的VLP方案显著提高了室内定位的准确性和稳定性.
  • 通过智能处理不同房间区域和估计高度,该系统克服了传统的基于RSSI的VLP的关键局限性.
  • 取得的厘米级准确度将这种方法定位为准确的室内导航和定位应用的有希望的解决方案.