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相关概念视频

Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

63
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...
63
Errors in Global Positioning System01:26

Errors in Global Positioning System

49
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,...
49
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

67
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,...
67
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

51
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...
51
Root-Locus Method01:19

Root-Locus Method

160
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
160
State Space Representation01:27

State Space Representation

214
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
214

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相关实验视频

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基于卡尔曼波器的参数自由状态估计,用于自动驾驶系统中GPS跟踪的注意学习.

Xue-Bo Jin1,2, Wei Chen1,2, Hui-Jun Ma1,2

  • 1Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
概括

本研究介绍了基于注意力学习的卡尔曼波器,用于GPS机动目标跟踪,克服了古典方法的局限性. 这种新的方法提高了状态估计的准确性,而不需要预定义的系统参数.

关键词:
卡尔曼过器可以过.变压器变压器变压器长期和短期内存网络网络.国家估计估计.轨迹跟踪 轨迹跟踪 轨迹跟踪

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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
  • 地理空间分析的研究.

背景情况:

  • 使用GPS操纵目标跟踪对于自动驾驶系统至关重要,但由于复杂的运动和未知的传感器/噪声特征而面临挑战.
  • 经典的卡尔曼过方法在GPS数据中与参数不确定性和未知的颜色噪声作斗争,降低了性能.
  • 准确的状态估计机动目标对于可靠的导航和自动驾驶车辆操作至关重要.

研究的目的:

  • 开发一种基于GPS的强大的机动目标定位和跟踪方法,克服传统方法的局限性.
  • 引入一种新的状态估计技术,利用注意力学习和在线参数估计.
  • 在存在复杂的动态和未知的GPS数据特征的情况下,提高状态估计的准确性和可靠性.

主要方法:

  • 一种基于卡尔曼波器的状态估计方法,通过变压器编码器和LSTM网络结合了注意力学习.
  • 在线估计系统模型参数使用预期最大化 (EM) 算法,由注意力学习输出驱动.
  • 将学习的系统,动态和测量特征集成到卡尔曼波器中以进行状态估计.

主要成果:

  • 提出的基于注意力学习的方法与经典和纯无模型网络方法相比,显示出更高的估计准确性.
  • 使用GPS模拟数据和Geolife北京车辆GPS轨迹数据集的实验验证证证了该方法的有效性.
  • 这种方法成功地解决了复杂的机动目标运动和未知的GPS数据属性所带来的挑战.

结论:

  • 开发的基于注意力学习的卡尔曼波器为实用机动目标跟踪应用提供了有效的解决方案.
  • 这种方法提供了准确的状态估计,而不依赖预定义的系统参数,提高了稳定性.
  • 这些发现有助于通过改进GPS跟踪能力来推进自动驾驶和导航系统.