Attitude-Correlated Frames Algorithms to Improve the Attitude Accuracy of the Star Tracker Under Highly Dynamic Conditions

The attitude accuracy of a star tracker decreases rapidly when star images become motion-blurred under dynamic conditions. To improve the performance of the star tracker, the attitude-correlated frames (ACF) approach concentrating on the features of the attitude transforms of adjacent star image frames, was proposed recently. It is effective in removing random noises and improving the attitude accuracy of the star tracker under different dynamic conditions and noise gray levels. In this paper, two simplified ACF algorithms are given and discussed. The effect of gyro noise is estimated and an optimal number of correlated frames is approximately calculated both for the ACF algorithms. Validation simulations are implemented and discussions are presented.