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Updated: Dec 30, 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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A higher-order robust correlation Kalman filter for satellite attitude estimation.

Xiaoqian Chen1, Lu Cao1, Pengyu Guo1

  • 1Research Center of Unmanned Systems Technology, National Innovation Institute of Defense Technology, Beijing 100071, China.

ISA Transactions
|January 18, 2020
PubMed
Summary

This study introduces a robust correlation Kalman filter (RCKF) for satellite attitude estimation, improving accuracy and handling unknown errors. The higher-order sigma RCKF effectively estimates satellite orientation despite modeling uncertainties.

Keywords:
Attitude estimationHigh-order filterModeling errorSatelliteSigma point

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Signal Processing

Background:

  • Satellite attitude estimation is crucial for mission success.
  • Unknown modeling errors challenge traditional Kalman filtering approaches.
  • Robustness and accuracy are key requirements for satellite navigation.

Purpose of the Study:

  • To develop a higher-order robust correlation Kalman filtering approach for satellite attitude estimation.
  • To address challenges posed by unknown modeling errors in satellite systems.
  • To enhance the accuracy and robustness of attitude estimation algorithms.

Main Methods:

  • Derivation of a robust correlation Kalman filter (RCKF) using the sequence orthogonal principle.
  • Design of a higher-order sigma version of the RCKF with a novel sigma point generation algorithm.
  • Incorporation of third and fourth central moment information into the filter's probability density function.

Main Results:

  • The proposed higher-order sigma RCKF demonstrates improved estimation accuracy.
  • The filter exhibits enhanced robustness in the presence of unknown modeling errors.
  • Simulation results confirm the effectiveness of the developed filter for satellite attitude estimation.

Conclusions:

  • The higher-order robust correlation Kalman filtering approach provides a superior solution for satellite attitude estimation.
  • The novel sigma point generation algorithm significantly enhances filter performance.
  • The developed method offers a reliable tool for precise satellite orientation determination.