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Related Concept Videos

Errors in Global Positioning System01:26

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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Types of Global Positioning System Surveys01:30

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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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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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Field Application of Global Positioning System01:28

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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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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo.

Suchanun Piriyasatit1,2, Ercan Engin Kuruoglu1,2, Mehmet Sinan Ozeren3

  • 1Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China.

Entropy (Basel, Switzerland)
|April 26, 2024
PubMed
Summary
This summary is machine-generated.

This study uses particle filtering to analyze GPS data, revealing hidden network dynamics and relationships in ground displacement time-series. This approach enhances understanding of geodetic observations for future anomaly detection.

Keywords:
GPS time-series analysisgeodeticsparticle filteringsequential Monte Carlospatiotemporal analysis

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Area of Science:

  • Geodesy and Geophysics
  • Earthquake Science

Background:

  • High-rate Global Positioning System (GPS) time-series data enable millimeter-level ground deformation modeling.
  • Current applications focus on crustal velocity fields and earthquake displacement detection, leaving inherent data relationships underexplored.

Purpose of the Study:

  • To develop a time-varying analysis of relationships within GPS displacement time-series networks.
  • To uncover underlying network dynamics and enhance understanding of geodetic observations.
  • To introduce a graph representation for improved relationship analysis.

Main Methods:

  • Sequential Monte Carlo method, specifically particle filtering (PF).
  • Time-varying analysis of multi-station GPS displacement data.
  • Graph representation for network relationship visualization.

Main Results:

  • Successful parameter tracking clarifying the dynamics of GPS displacement observations.
  • Demonstrated effectiveness using 1-Hz GEONET GNSS data from the 2011 Tohoku-Oki earthquake.
  • Identified previously unexplored relationships within GPS time-series networks.

Conclusions:

  • Particle filtering offers a novel approach to analyzing complex geodetic network dynamics.
  • The proposed graph representation aids in understanding inter-station relationships.
  • Findings have potential for future applications in detecting anomalous ground displacements.