Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions

Abstract

Star trackers and gyroscopes are the two most widely used attitude measurement devices in spacecrafts. The star tracker is supposed to have the highest accuracy in stable conditions among different types of attitude measurement devices. In general, to detect faint stars and reduce the size of the star tracker, a method with long exposure time method is usually used. Thus, under dynamic conditions, smearing of the star image may appear and result in decreased accuracy or even failed extraction of the star spot. This may cause inaccuracies in attitude measurement. Gyros have relatively good dynamic performance and are usually used in combination with star trackers. However, current combination methods focus mainly on the data fusion of the output attitude data levels, which are inadequate for utilizing and processing internal blurred star image information. A method for tracking deep coupling stars and MEMS-gyro data is proposed in this work. The method achieves deep fusion at the star image level. First, dynamic star image processing is performed based on the angular velocity information of the MEMS-gyro. Signal-to-noise ratio (SNR) of the star spot could be improved, and extraction is achieved more effectively. Then, a prediction model for optimal estimation of the star spot position is obtained through the MEMS-gyro, and an extended Kalman filter is introduced. Meanwhile, the MEMS-gyro drift can be estimated and compensated though the proposed method. These enable the star tracker to achieve high star centroid determination accuracy under dynamic conditions. The MEMS-gyro drift can be corrected even when attitude data of the star tracker are unable to be solved and only one navigation star is captured in the field of view. Laboratory experiments were performed to verify the effectiveness of the proposed method and the whole system.