开发UWB/Wi-Fi RTT范围的先进定位技术,用于个人移动应用
Harris Perakis1, Vassilis Gikas1, Günther Retscher2
1School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 157 80 Zographos, Greece.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究使用超宽带 (UWB) 和Wi-Fi往返时间 (RTT) 测量来增强行人定位. 整合这些技术可显著提高个人和群体的定位准确性,即使有间歇性信号损失.
科学领域:
- 室内定位系统 室内定位系统
- 无线传感器网络 无线传感器网络
- 无处不在的计算无处不在的计算
背景情况:
- 智能设备是日常生活中不可或缺的组成部分,使导航和基于位置的服务 (LBS) 成为可能.
- 准确的行人定位仍然是一个挑战,特别是在复杂的环境中.
- 现有的方法经常与信号干扰和可用性作斗争.
研究的目的:
- 调查使用超宽带 (UWB) 和Wi-Fi往返时间 (RTT) 进行行人定位.
- 开发和评估用于纠正基于射频的双向距离测距 (TWR) 的距离错误的方法.
- 为集体行人定位创建一个强大的协作导航 (CP) 引擎.
主要方法:
- 使用真实观察和模拟数据设计的场景用于UWB和Wi-Fi RTT测量.
- 应用了聚变算法来结合测距和定位估计数据.
- 对CP的UWB和Wi-Fi RTT测量的杆化混合性质.
- 开发了用于TWR系统中距离错误校正的模型.
- 为本地化算法实现了一个P2I/P2P配置.
主要成果:
- 整合UWB和Wi-Fi RTT在位置真实性方面实现了分别为74%和54%的性能改进.
- 拟议的P2I/P2P算法提高了高达10%的位置准确性,并提供了连续的可用性.
- 在短损失事件 (仅P2P) 中,观察到高达53%的位置准确度的显著改善.
结论:
- 提出的方法有效地提高了行人定位的准确性.
- 混合UWB和Wi-Fi RTT系统与单个技术相比提供了更高的性能.
- 开发的CP引擎表现出对集团本地化的稳定性,即使在具有挑战性的条件下.
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