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Related Concept Videos

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Errors in Global Positioning System

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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,...
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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...
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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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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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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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Introduction to Global Positioning System01:30

Introduction to Global Positioning System

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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,...
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Methods of Obtaining Topography01:25

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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A Machine Learning-Based Tropospheric Prediction Approach for High-Precision Real-Time GNSS Positioning.

Jianping Chen1, Yang Gao1

  • 1Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

A new machine learning method predicts tropospheric errors for Global Navigation Satellite System (GNSS) applications in real-time. This approach overcomes latency issues with existing data, achieving high accuracy for precise positioning.

Keywords:
FFNNLSTMdeep learningmachine learningneural networktroposphere prediction

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Area of Science:

  • Geodesy and Geomatics
  • Atmospheric Science
  • Machine Learning Applications

Background:

  • Tropospheric errors significantly impact high-precision Global Navigation Satellite System (GNSS) positioning, reaching meters.
  • Current real-time troposphere correction services have limited regional availability.
  • Existing post-mission data from the International GNSS Service (IGS) has a latency of 1-2 weeks, unsuitable for real-time use.

Purpose of the Study:

  • To develop a real-time troposphere prediction method for GNSS applications.
  • To overcome the latency limitations of current tropospheric correction data.
  • To enable high-precision positioning by mitigating real-time tropospheric errors.

Main Methods:

  • Developed a real-time troposphere prediction model utilizing machine learning techniques.
  • Leveraged International GNSS Service (IGS) post-processing products as input data.
  • Validated the prediction method using a year-long dataset.

Main Results:

  • Achieved a root mean square error (RMSE) of 2 cm for tropospheric predictions.
  • Demonstrated the suitability of the developed method for real-time applications.
  • Successfully eliminated the long latency associated with traditional post-mission data.

Conclusions:

  • The proposed machine learning-based method provides accurate, real-time troposphere predictions for GNSS.
  • This advancement supports high-precision positioning applications by addressing a key error source.
  • The method offers a viable alternative to existing services with limited coverage or high latency.