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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

Operation of the Collaborative Composite Manufacturing CCM System
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根据动态突变感知和协作校正的自适应卡尔曼过定位校准方法.

Zijia Huang1, Qiushi Xu2, Menghao Sun2

  • 1National Key Laboratory of Multi-Domain Data Collaborative Processing and Control, Xi'an 710068, China.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了自适应的卡尔曼选方法,以提高无人机在复杂的电磁环境中的无人群导航精度. 该方法通过监测系统突变和协作纠正轨迹来增强定位.

关键词:
适应式卡尔曼过器合作纠正纠正协作纠正动态突变感知 动态突变感知

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 导航和定位技术 导航和定位技术
  • 减轻电磁干扰的影响

背景情况:

  • 无人小群导航系统由于复杂的电磁环境中的动态噪声而面临精度降低.
  • 现有的卡尔曼过方法在与突然的噪音作斗争,影响协作定位性能.

研究的目的:

  • 提出一种自适应的卡尔曼过方法,用于在无人群导航中增强定位和校准.
  • 为了应对复杂的电磁环境中的动态突发噪声的挑战.

主要方法:

  • 开发了一种自适应的卡尔曼过方法,包括动态突变感知和协作校正.
  • 使用动态值检测机制实现加速和速度突变的实时监控.
  • 利用多维缩放分析进行轨迹相似性计算和协作状态校正,自适应地调整协差矩阵.

主要成果:

  • 与传统的扩展卡尔曼波器算法相比,提出的方法证明了更好的定位准确性.
  • 使用模拟和现实世界的数据进行实验验证,证实了自适应过技术的有效性.
  • 成功地减轻了复杂的电磁干扰对无人机群协作定位的影响.

结论:

  • 适应性卡尔曼过方法为在电磁干扰下强大的无人小群导航提供了有效的解决方案.
  • 实时适应和协作校正对于在动态环境中保持高定位精度至关重要.
  • 这项研究为无人驾驶系统中可靠的协作定位提供了重大进展.