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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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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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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
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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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Related Experiment Video

Updated: Aug 2, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking.

Iñigo Cortés1,2, Johannes Rossouw van der Merwe3, Elena Simona Lohan2

  • 1Satellite Based Positioning Systems Department, Fraunhofer IIS, Nordostpark 84, 90411 Nuremberg, Germany.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study introduces a simplified Kalman filter for robust Global Navigation Satellite System (GNSS) signal tracking. An adaptive loop-bandwidth control algorithm enhances synchronization in challenging, dynamic environments.

Keywords:
adaptive tracking techniquesfull direct-state Kalman filter (DSKF)global navigation satellite system (GNSS)lookup table direct-state Kalman filter (LUT-DSKF)loop-bandwidth control algorithm (LBCA)

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Area of Science:

  • Signal Processing
  • Navigation Systems
  • Control Theory

Background:

  • Direct-state Kalman filters (DSKF) offer robust Global Navigation Satellite System (GNSS) signal tracking but incur high computational costs.
  • DSKF performance degrades in dynamic environments due to changing measurement statistics (noise, multi-path, non-line-of-sight).

Purpose of the Study:

  • To develop a low-complexity, adaptive DSKF for improved GNSS signal tracking robustness.
  • To enhance real-time GNSS receiver performance in challenging signal conditions.

Main Methods:

  • Derivation of a full lookup table (LUT)-DSKF by leveraging Kalman gain steady-state convergence.
  • Extension of the loop-bandwidth control algorithm (LBCA) for adaptive response time control.
  • Implementation and testing of the adaptive full LUT-DSKF in a GNSS hardware receiver.

Main Results:

  • The adaptive full LUT-DSKF demonstrates improved synchronization robustness in time-varying scenarios.
  • Simulated evaluations across various dynamics and noise levels validated the filter's performance.
  • The LBCA, particularly in the FLL-assisted-PLL (FAP) configuration, proved crucial for maintaining position, velocity, and time (PVT) fixes under high dynamics.

Conclusions:

  • The proposed adaptive full LUT-DSKF offers a computationally efficient solution for robust GNSS signal tracking.
  • The LBCA is critical for maintaining PVT accuracy in dynamic GNSS environments.
  • This adaptive tracking technique significantly enhances GNSS receiver reliability.