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相关概念视频

Errors in Global Positioning System01:26

Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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相关实验视频

Updated: May 10, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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神经动力强大的自适应性UWB定位算法与NLOS缓解.

Yanxu Liu1, Enwen Hu2, Yudong Chen3

  • 1College of Computer and Information, Dezhou University, Dezhou, 253023, China. liuyanxu@dzu.edu.cn.

Scientific reports
|April 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的,强大的自适应定位算法,用于混合视线 (LOS) 和非视线 (NLOS) 条件的室内环境. 这种新方法可以提高定位的准确性和稳定性,而无需先前了解NLOS偏差.

关键词:
在NLOS减轻NLOS减轻神经动力学神经动力学预测神经网络的预测性神经网络.强大的本地化.在UWB定位,UWB定位.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 信号处理 信号处理
  • 计算机视觉 计算机视觉

背景情况:

  • 由于混合的视线 (LOS) 和非视线 (NLOS) 条件,准确的室内定位具有挑战性.
  • 现有的算法通常需要先前了解NLOS偏差或LOS/NLOS识别,从而限制了它们的适用性.
  • 在严峻的NLOS环境中,稳定性和准确性仍然是室内定位系统的关键问题.

研究的目的:

  • 开发一个强大的适应性定位算法,用于室内环境与混合LOS和NLOS.
  • 为了应对提出的最大-最小优化估计器的非凸性所带来的挑战.
  • 提高本地化准确性和稳定性,特别是在具有挑战性的NLOS场景中.

主要方法:

  • 提出了一个最大-最小的优化估计器和一个适应性损失函数,以进行可靠的估计.
  • 采用神经动力学方法来解决非凸的优化问题.
  • 引入了自适应距离误差上限方法,以提高本地化模型的准确性.

主要成果:

  • 神经动力学方法有效地解决了非凸的优化问题.
  • 与现有方法相比,该算法证明了优越的本地化准确性和稳定性.
  • 在严峻的NLOS环境中,性能尤其显著,通过模拟和现实世界的测试来验证.

结论:

  • 拟议的强大的自适应定位算法为混合LOS/NLOS环境中的室内定位提供了显著的改进.
  • 该方法在没有先前的LOS/NLOS信息或NLOS偏差大小的情况下运行的能力提高了其实际实用性.
  • 这项工作为室内定位提供了更准确,更可靠的解决方案,特别是在不利的信号传播条件下.