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Errors in Global Positioning System01:26

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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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Related Experiment Video

Updated: Aug 25, 2025

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Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises.

Murillo Villa1, Bruno Ferreira1, Nuno Cruz1

  • 1INESC TEC-Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 378, 4200-465 Porto, Portugal.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study optimizes sensor placement for underwater vehicle localization using a genetic algorithm. The method precisely determines optimal sensor configurations, minimizing localization errors for improved underwater navigation.

Keywords:
Fisher information matrixgenetic algorithmoptimal sensor placementunderwater vehicle

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Area of Science:

  • Robotics
  • Ocean Engineering
  • Signal Processing

Background:

  • Sensor geometry significantly impacts source localization accuracy.
  • Optimal sensor placement depends on measurement types, noise, source position, and performance criteria.
  • Underwater vehicle localization requires robust sensor configurations.

Purpose of the Study:

  • To determine the optimal sensor placement in a 2D plane for localizing a 3D underwater vehicle.
  • To develop and analyze a genetic algorithm for single and multi-objective sensor placement problems.
  • To evaluate localization performance using the Fisher information matrix under distance-dependent noise.

Main Methods:

  • A genetic algorithm was developed for optimizing sensor positions.
  • Single-objective optimization used the average maximum eigenvalue of the inverse Fisher information matrix.
  • Multi-objective optimization estimated the Pareto front for common Fisher information matrix criteria.
  • Distance-dependent covariances modeled measurement noises.

Main Results:

  • The algorithm achieved deviations less than 0.1% compared to a known optimal solution with constant noise.
  • Sensor positioning evolution was analyzed for different optimization criteria.
  • Localization performance was evaluated for lawn-mower and spiral descent maneuvers.
  • Results were presented for restricted sensor placement scenarios.

Conclusions:

  • The developed genetic algorithm effectively optimizes sensor placement for 3D underwater vehicle localization.
  • The approach handles distance-dependent noise and various maneuver types.
  • This work provides a framework for enhancing underwater navigation system accuracy through optimal sensor deployment.