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相关概念视频

Errors in Global Positioning System01:26

Errors in Global Positioning System

307
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,...
307
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

436
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,...
436
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

286
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...
286
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

309
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...
309
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

364
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...
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Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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相关实验视频

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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在RBF神经网络的帮助下,在城市环境中强大的自适应GNSS/INS集成导航算法.

Jin Wang1,2, Ruoyi Li1, Rui Tu1

  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
概括

本研究介绍了一种改进的全球导航卫星系统 (GNSS) /惯性导航系统 (INS) 算法. 这种新方法通过使用强大的自适应卡尔曼波器 (RAKF) 和辐射基函数 (RBF) 网络,提高了城市地区的定位精度,即使GNSS信号丢失.

关键词:
在GNSS的位置增量预测.在GNSS/INS中使用.在RBF神经网络中.强大的自适应卡尔曼波器城市导航和定位.

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科学领域:

  • 导航系统工程 导航系统工程
  • 在导航中使用人工智能
  • 用于定位的信号处理.

背景情况:

  • 城市环境中的精确定位受到全球导航卫星系统 (GNSS) 信号减弱和中断的挑战.
  • 只有惯性导航系统 (INS) 在GNSS中断时会受到准确性降低.
  • 现有的集成导航系统需要改进,以便在复杂的场景中提供强大的性能.

研究的目的:

  • 开发一个先进的全球导航卫星系统/惯性导航系统 (GNSS/INS) 综合导航算法.
  • 在GNSS信号退化条件下提高定位准确性和稳定性,特别是在城市环境中.
  • 为了减轻GNSS信号中断对导航性能的影响.

主要方法:

  • 开发了一个混合框架,将强大的自适应卡尔曼波器 (RAKF) 与辐射基函数 (RBF) 神经网络相结合.
  • 根据GNSS数据质量指标 (PDOP,卫星计数,信号质量),RAKF可以自适应地调整测量噪声协差.
  • RBF网络预测伪位置增量,以替代停机期间缺失的GNSS测量.

主要成果:

  • 拟议的RBF辅助RAKF (RBF-RAKF) 在GNSS中断期间实现了0.94m (北),1.02m (东) 和0.21m (下) 的根平均平方 (RMS) 定位误差.
  • 与传统的扩展卡尔曼波器 (EKF),标准RAKF和RBF辅助卡尔曼波器 (RBF-KF) 相比,在定位准确度上有超过90%的改进.
  • 在严重的GNSS信号退化下,保持了仪表水平和子仪表垂直精度.

结论:

  • 在具有挑战性的城市环境中,RBF-RAKF算法提供了稳定和高精度的导航性能.
  • 混合方法有效地克服了GNSS信号中断和退化所带来的局限性.
  • 这种方法显著提高了自主应用的集成导航系统的可靠性.