Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

36
GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
36

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

[Experimental study of the eyelid reconstruction in situ with the acellular xenogeneic dermal matrix].

Zhonghua zheng xing wai ke za zhi = Zhonghua zhengxing waike zazhi = Chinese journal of plastic surgery·2007
Same author

[Mutation analysis of GCH1 gene in Chinese patients with dopa responsive dystonia].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2007
Same author

[Screening and characterization of marine bacteria with antibacterial and cytotoxic activities, and existence of PKS I and NRPS genes in bioactive strains].

Wei sheng wu xue bao = Acta microbiologica Sinica·2007
Same author

[Collateral supply in patients with severe carotid stenosis].

Zhonghua yi xue za zhi·2007
Same author

[Changes of sleep architecture in patients with narcolepsy].

Zhonghua yi xue za zhi·2007
Same author

[Combined anterior and posterior approach for cervical fracture-dislocation with ankylosing spondylitis].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2007

相关实验视频

Updated: May 8, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.7K

扩展目标跟踪与基于GPR-AUKF的移动性

Renli Zhang1, Yan Zhang2, Jintao Chen1

  • 1School of Aeronautics and Astronautics, Sun Yat-sen University, 518107, Shenzhen, China.

Heliyon
|December 24, 2024
PubMed
概括

这项研究引入了一种新的高斯过程回归适应无气味卡尔曼波器 (GPR-AUKF),用于精确追踪扩展目标. 该GPR-AUKF方法有效地处理非线性和时间变化的噪声,优于传统的过器.

关键词:
适应性无气味的卡尔曼波器 适应性无气味的卡尔曼波器预期最大化算法 预期最大化算法扩展目标扩展目标高斯过程回归的高斯过程回归.时间变化的噪音.

更多相关视频

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software
10:15

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software

Published on: June 20, 2013

20.1K
Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K

相关实验视频

Last Updated: May 8, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.7K
Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software
10:15

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software

Published on: June 20, 2013

20.1K
Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K

科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 估计扩展目标的动力状态和范围是复杂的,特别是对于移动目标.
  • 传统的扩展卡尔曼波器 (EKF) 可能会在高非线性和时间变化的噪声中扎.
  • 准确的跟踪需要适应不断变化的测量噪声共变性.

研究的目的:

  • 开发一种先进的过技术,用于高精度跟踪扩展目标.
  • 通过结合高斯过程回归和适应能力来改进现有的卡尔曼波器方法.
  • 在动态环境中解决恒定测量噪声共变性假设的局限性.

主要方法:

  • 在高斯过程回归 (GPR) 中嵌入无气味卡尔曼波器 (UKF),以处理非线性.
  • 开发一个与GPR (GPR-AUKF) 集成的自适应无气味卡尔曼波器 (AUKF).
  • 在GPR-AUKF中使用预期最大化 (EM) 算法来实时更新测量噪声共变率和目标状态估计.

主要成果:

  • 拟议的GPR-AUKF算法在追踪扩展目标方面表现出卓越的准确性.
  • 实验结果证实了与传统方法相比,GPR-AUKF的稳定性.
  • 由于GPR-AUKF的自适应性,可以有效地管理时间变化的测量噪声共变性.

结论:

  • 在具有挑战性的环境中,GPR-AUKF算法为扩展目标跟踪提供了显著的进步.
  • 该方法为具有非线性动态和不确定的噪声特征的场景提供了可靠的解决方案.
  • 测量噪声共变率的实时调整是提高性能的关键.