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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

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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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Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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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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Vector Algebra: Graphical Method01:10

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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相关实验视频

Updated: Jul 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

568

室内定位算法基于一个高顺序图神经网络的神经网络.

Xiaofei Kang1, Xian Liang1, Qiyue Liang1

  • 1College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

一个新的高阶图形神经网络 (HoGNNLoc) 提高了室内定位的准确性和稳定性. 这种机器学习方法有效地处理复杂的无线电信号数据,优于可靠定位的现有方法.

关键词:
无线网络指纹的指纹邻矩阵是一个邻矩阵.图表神经网络的神经网络室内局部化 室内局部化

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相关实验视频

Last Updated: Jul 13, 2025

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 信号处理 信号处理

背景情况:

  • 机器学习指纹是常见的室内定位.
  • 现有的模型与来自不同环境的非结构化指纹数据作斗争.
  • 在动态的室内环境中需要提高准确性和稳定性.

研究的目的:

  • 提出一个新的室内定位算法,HoGNNLoc.
  • 提高室内定位的准确性和稳定性.
  • 解决指纹数据当前机器学习模型的局限性.

主要方法:

  • 开发了一个高级图形神经网络 (HoGNNLoc) 用于室内定位.
  • 使用接入点空间位置构建了一个图形结构.
  • 使用完全连接的网络进行位置回归预测.

主要成果:

  • 在自己构建的数据集上,在80%的CDF下,在1.29m以内实现了定位准确性.
  • 与KNN,DNN,SGC和GAT (高达59.2%) 相比,显示了显著的准确性改进.
  • 保持稳定的性能,即使指纹数据减少了30%.

结论:

  • HoGNNLoc提供卓越的室内定位性能.
  • 该算法有效地从复杂的指纹数据中提取特征.
  • HoGNNLoc显示出对强大而准确的室内定位系统的承诺.