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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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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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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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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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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Updated: Sep 22, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check.

Tomohiro Ozeki1, Nobuaki Kubo1

  • 1Department of Maritime Systems Engineering, Tokyo University of Marine Science and Technology, Tokyo, Japan.

Frontiers in Robotics and AI
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

Detecting non-line-of-sight (NLOS) signals improves Global Navigation Satellite System (GNSS) positioning accuracy in urban areas. A new method using a support vector machine (SVM) classifier effectively reduces errors caused by NLOS signals.

Keywords:
DGNSSGNSSNLOSmultipathpseudorange residualsupport vector machine

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Area of Science:

  • Geomatics Engineering
  • Satellite Navigation Systems
  • Machine Learning Applications

Background:

  • Global Navigation Satellite System (GNSS) positioning is crucial for autonomous driving and construction.
  • Dense urban environments present challenges due to signal obstruction and multipath effects.
  • Distinguishing between line-of-sight (LOS) and non-line-of-sight (NLOS) signals is vital for accuracy.

Purpose of the Study:

  • To develop and evaluate a novel method for detecting NLOS signals in GNSS positioning.
  • To improve the reliability and accuracy of GNSS positioning in challenging urban settings.
  • To leverage machine learning for enhanced multipath signal identification.

Main Methods:

  • Utilized a support vector machine (SVM) classifier for NLOS signal detection.
  • Developed unique features from receiver independent exchange format data and GNSS pseudorange residuals.
  • Conducted static tests in downtown Tokyo with high-rise buildings to simulate urban conditions.

Main Results:

  • The proposed SVM classifier combined with GNSS pseudorange residual checks effectively reduced positioning errors caused by NLOS signals.
  • Horizontal positioning errors within 10 meters were improved by over 80% in static tests.
  • The method demonstrated significant error reduction by identifying and excluding satellites with NLOS signals.

Conclusions:

  • The SVM-based NLOS detection method offers a robust solution for improving GNSS accuracy in urban environments.
  • Accurate NLOS signal identification is key to overcoming multipath interference in satellite navigation.
  • This approach enhances the viability of GNSS for critical applications like autonomous systems.