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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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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: Jan 2, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
04:13

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning

Valentín Barral1, Carlos J Escudero1, José A García-Naya1

  • 1CITIC Research Center, Campus de Elviña, Universidade da Coruña (University of A Coruña), 15071 A Coruña, Spain.

Sensors (Basel, Switzerland)
|December 15, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning techniques effectively detect non-line-of-sight errors in ultra-wideband indoor positioning systems. This improves accuracy even when training and testing occur in different real-world scenarios.

Keywords:
NLOS detectionUWBindoor location algorithmsmachine learningneural networks

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Area of Science:

  • Robotics
  • Signal Processing
  • Machine Learning

Background:

  • Radio frequency indoor positioning systems suffer from multipath propagation, degrading accuracy.
  • Ultra-wideband (UWB) ranging is particularly susceptible to errors from secondary signal paths.
  • Positioning algorithms using raw ranging data without accounting for multipath face significant errors.

Purpose of the Study:

  • To analyze the performance of localization systems combining algorithms with machine learning.
  • To classify and mitigate propagation effects like non-line-of-sight (NLOS) using ML.
  • To evaluate system performance in cross-scenarios with distinct training and testing environments.

Main Methods:

  • Implementing machine learning techniques for classification and mitigation of propagation effects.
  • Utilizing low-cost ultra-wideband (UWB) devices for data acquisition.
  • Testing the system in real-world cross-scenarios where training and testing data originate from different environments.

Main Results:

  • Machine learning techniques demonstrate suitability for detecting non-line-of-sight (NLOS) ranging values.
  • The proposed approach shows effectiveness even when training and testing data are from disparate scenarios.
  • Performance analysis indicates improved localization accuracy through ML-based mitigation.

Conclusions:

  • Machine learning is a viable tool for enhancing the robustness of UWB indoor positioning systems.
  • ML-based mitigation of multipath effects, specifically NLOS, is crucial for accurate localization.
  • The cross-scenario validation confirms the adaptability and practical applicability of the developed techniques.