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Related Experiment Video

Updated: Feb 24, 2026

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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An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors.

Jian Li¹, Xinguo Wei², Guangjun Zhang³

¹School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China. lijian_0355@buaa.edu.cn.

Sensors (Basel, Switzerland)

|August 22, 2017

Summary

This summary is machine-generated.

This study introduces an improved extended Kalman filtering algorithm for star sensor attitude tracking, enhancing reliability during high dynamic maneuvers. The novel star mapping technique significantly speeds up star recognition for more efficient attitude estimation.

Keywords:

attitude tracking extended Kalman filter star sensor

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Area of Science:

Aerospace Engineering
Navigation Systems
Robotics

Background:

Star sensors are crucial for spacecraft attitude determination.
Existing attitude tracking algorithms struggle with high dynamic conditions, leading to performance degradation.
Reliable and efficient attitude tracking is essential for mission success.

Purpose of the Study:

To develop a robust attitude tracking algorithm for star sensors operating under high attitude dynamics.
To improve the efficiency and reliability of star sensor-based attitude estimation.
To introduce a star mapping technique for accelerated star recognition.

Main Methods:

Modeling the star sensor as a nonlinear stochastic system.
Utilizing an extended Kalman filter for attitude and angular velocity estimation.
Implementing a star mapping method with a catalog partition table for rapid star identification.

Main Results:

The proposed extended Kalman filtering algorithm demonstrates improved performance in high dynamic scenarios.
The star mapping technique significantly reduces the time required for star recognition.
Software simulations and experimental validation confirm the algorithm's efficiency and reliability.

Conclusions:

The developed algorithm offers a reliable solution for star sensor attitude tracking in dynamic environments.
The star mapping enhancement provides a computationally efficient approach to star recognition.
The method is validated for practical application in spacecraft attitude determination.