How to improve the attitude accuracy of the star sensor under dynamic conditions: A review

Abstract

Star sensors experience significant degradation in attitude measurement accuracy under dynamic conditions, primarily caused by motion-induced star image blurring and diminished effective stars. This review analyzes the underlying mechanisms responsible for accuracy deterioration in dynamically perturbed star sensors. As the first comprehensive survey in this field, the paper proposes a novel taxonomy classifying existing mitigation strategies into two paradigm categories: active and passive deblurring techniques and motion blur compensation methods. We critically analyzed representative approaches within each category, including optical system optimization, multi-FOV configurations, servo stabilization platforms, and algorithm-driven solutions such as image restoration and attitude-correlated frame processing. A comparative evaluation highlights the advantages and limitations of these methods through quantitative performance metrics and implementation constraints. Furthermore, the study establishes selection criteria emphasizing that optimal strategy adoption must consider application-specific requirements. This work provides both a technical reference for star sensor designers and a framework guiding future research directions in high-dynamics attitude determination.