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相关概念视频

Errors in Global Positioning System01:26

Errors in Global Positioning System

32
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,...
32
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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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...
47
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

45
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,...
45
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

25
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...
25
Linear time-invariant Systems01:23

Linear time-invariant Systems

209
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
209

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Updated: May 28, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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面向GNSS多路径LSTM训练模型的可解释的人工智能

He-Sheng Wang1, Dah-Jing Jwo1, Zhi-Hang Gao1

  • 1Department of Communications, Navigation, and Control Engineering, National Taiwan Ocean University, 2 Peining Road, Keelung 202301, Taiwan.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种可解释的深度学习框架,用于全球导航卫星系统 (GNSS) 多路径检测. 层级相关性传播 (LRP) 揭示信号异常,提高导航系统的可靠性.

关键词:
在GNSS中使用GNSS.可以解释性的解释性.层层的相关性传播传播.长期短期记忆 长期短期记忆多路径的多路径.

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科学领域:

  • 地理学工程 工程地质学
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 全球导航卫星系统 (GNSS) 越来越多地使用深度学习来处理信号.
  • 深度学习模型中缺乏可解释性对安全关键的GNSS应用构成风险.
  • 多路径效应是GNSS信号分析的一个重大挑战.

研究的目的:

  • 开发一种可解释的深度学习框架,用于在GNSS中检测多路径效应.
  • 提高GNSS应用中使用的深度学习模型的可解释性.
  • 建立一种用于GNSS信号中异常检测的新方法.

主要方法:

  • 为GNSS可观测物开发了一个可解释的长短期内存 (LSTM) 架构.
  • 适应层级相关性传播 (LRP) 用于GNSS信号分析,用于归因模型决策.
  • 与信号异常相关的LRP相关性得分用于异常检测.

主要成果:

  • 在GNSS参数中,LSTM模型实现了高预测准确性,同时保持了可解释性.
  • 在异常信号条件下,LRP相关性得分始终增加 (7.34%-32.48%).
  • 观察到的具体增加:多路径参数 (7.34-8.81%),载体与噪声的比率 (12.50-32.48%),升高角度 (16.10%).

结论:

  • 基于LRP的分析增强了GNSS信号质量监测和完整性评估.
  • 拟议的方法为检测和分析GNSS信号异常提供了实际框架.
  • 这项工作有助于开发更可靠和值得信赖的导航系统.