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相关概念视频

Field Application of Global Positioning System01:28

Field Application of Global Positioning System

322
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Errors in Global Positioning System01:26

Errors in Global Positioning System

338
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,...
338
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

347
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...
347
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

465
The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
465
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

379
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
379
Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

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CMAP Scan MUNE MScan - A Novel Motor Unit Number Estimation MUNE Method
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基于改进的灰色预测模型的GNSS/MEMS IMU阵列融合定位方法的研究.

Yihao Chen1, Jieyu Liu1, Weiwei Qin2

  • 1College of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China.

Micromachines
|September 27, 2025
PubMed
概括

这项研究引入了一种改进的汽车导航灰色预测模型,增强全球导航卫星系统 (GNSS) /MEMS IMU融合. 该方法可以提高定位准确性,特别是在GNSS信号拒绝期间.

关键词:
拒绝GNSS的拒绝方式MEMS IMU 阵列中的 MEMS IMU 阵列.适应性融合适应性融合灰色的预测模型车辆导航 车辆导航

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 导航系统 导航系统
  • 信号处理 信号处理

背景情况:

  • 全球导航卫星系统 (GNSS) 信号容易对车辆导航产生干扰和阻塞,降低定位准确度.
  • 传统的灰色预测模型难以处理车辆的复杂运动数据.
  • 准确的实时定位对于自动驾驶和辅助驾驶系统至关重要.

研究的目的:

  • 为车辆开发一个增强的GNSS/MEMS IMU融合本地化方法.
  • 提高定位准确度和可靠性,特别是在GNSS信号拒绝条件下.
  • 在动态环境中克服现有的灰色预测模型的局限性.

主要方法:

  • 一个多功能融合GNSS信任评估算法实时评估GNSS数据的可靠性.
  • 一个改进的灰色预测模型结合了动态背景值优化和复杂运动数据的残余序列补偿.
  • 适应性融合框架集成了GNSS和MEMS IMU数据,使用灰色模型预测作为GNSS中断期间的虚拟测量.

主要成果:

  • 改进的灰色预测模型在直线,转和加速场景中分别实现了比传统GM(1,1) 模型高出31%,52%,45%的准确性.
  • 与纯惯性导航系统 (INS) 方法相比,在30秒的GNSS拒绝期间,定位精度提高了79%以上.
  • 拟议的方法表明对车辆运动状态变化的灵敏度提高,并改善了非线性运动预测.

结论:

  • 拟议的GNSS/MEMS IMU融合方法显著提高了车辆定位的准确性和稳定性.
  • 改进的灰色预测模型有效地处理复杂的车辆动态和GNSS信号中断.
  • 这种方法为面临间歇性或拒绝GNSS可用性的导航系统提供了可靠的解决方案.