Results from GRACE-FO attitude determination based on Kalman filter

The gravity satellite attitude affects the accuracy of the time-variable gravity field model through non-conservative force transformation and K-Band Ranging (KBR) antenna phase center correction. Therefore, how to obtain high-precision attitude data is an important research content of raw payload data processing. Each satellite of GRACE Follow-On (GRACE-FO) is equipped with three star cameras and an inertial measurement unit (IMU) to measure satellite attitude data. The star cameras are sensitive to the low frequency of the spacecraft attitude, the IMU is sensitive to the high frequency of the spacecraft attitude, and we can obtain a high-precision attitude data product by fusing these types of data. Thus, based on the extended Kalman filter, we first derive the attitude Kalman filter method with quaternion and gyroscope drift parameters as state variables. Then, the GRACE-FO Level-lA data is used to verify whether the attitude Kalman filter proposed in this paper is feasible, and the influence on the accuracy of the recovered time-varying gravity field model is analyzed. For star cameras data processing, the high-frequency noise can be reduced by combining multiple star cameras, especially the accuracy of the combing three star cameras data is slightly better than the results of the two star cameras. For the fusion of the star camera and IMU, the attitude data calculated in this paper completely integrate the angular velocity information measured by the IMU, the accuracy is at least 3 times higher than the official institution, and the noise level at 0. 01 similar to 0. 1 Hz is about an order of magnitude lower. For the recovered time-varying gravity field, from the perspective of the degree variance and equivalent water height, the error of the recovered time-varying gravity field model caused by the difference attitude data is about 5% at the early mission, and the accuracy of the recovered time-varying gravity field is basically the same when entering the stable period of the mission. At present, the accuracy of attitude data has little effect on the accuracy of the recovered gravity field model, and the restriction of the accuracy of the time-varying gravity field model mainly comes from other errors.

Automated summary

This study investigates the influence of gravity satellite attitude on the accuracy of the time-variable gravity field model, and proposes a method for processing attitude data based on the extended Kalman filter. By fusing star camera and IMU data, high-precision attitude data products can be obtained. In addition, the impact of attitude data on the accuracy of the recovered gravity field model and the limitations of other errors on the accuracy of the time-variable gravity field model are analyzed.