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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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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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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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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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Related Experiment Video

Updated: May 31, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of

Salwa Sahnoun1,2, Rihab Souissi1,2,3, Sirine Chiboub1,2,3

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Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

The Recurrent Neural Network (RNN) model offers superior performance for indoor positioning systems, achieving high localization accuracy and fast processing times. This study compared various AI models, finding RNN to be the most effective for mobile tracking.

Keywords:
AIANNICM-20948 sensorKalman filterLSTMRNNRSSI measurementsindoor positioning systemsneural networks

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Sensor Fusion

Background:

  • Indoor positioning systems (IPS) are crucial for mobile tracking and navigation.
  • Existing IPS often face challenges with accuracy and processing speed.
  • AI models offer potential for enhanced IPS performance.

Purpose of the Study:

  • To comparatively evaluate AI models for indoor positioning.
  • To assess localization accuracy and processing time of different AI algorithms.
  • To identify optimal AI models for mobile tracking applications.

Main Methods:

  • Comparative analysis of Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and Kalman filters.
  • Utilized Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor data.
  • Implemented data cleaning and feature selection techniques for error reduction.

Main Results:

  • The Recurrent Neural Network (RNN) model demonstrated the best performance.
  • Achieved a localization error of 0.247 m with a processing delay of 0.077 s.
  • Evaluated performance within a 12 m × 9.5 m area using four anchors.

Conclusions:

  • Recurrent Neural Networks (RNNs) are highly effective for indoor positioning.
  • Model selection based on test and validation data is critical for effective mobile tracking.
  • AI advancements significantly improve localization accuracy and efficiency in IPS.