Combining Star Tracker and Gyroscope for Remote Sensing Satellite Attitude Determination: Algorithm Design and Performance Evaluation With Real Data

The precise satellite attitude determination is the basis of imagery positioning, which is crucial for remote sensing satellites to complete various observation missions. The complementary characteristics of star trackers and gyroscopes necessitate their combination to achieve high-precision attitude determination. Given that simulation data fail to fully replicate real space conditions, a practical adaptive fusion attitude determination scheme is proposed based on measured data from the TianHui-1 (TH-1) satellite, an optical remote sensing satellite in China, including fault detection and isolation, precise data quality control, and model observability analysis. The invariance of the included angle between the star trackers is employed to detect faults in sensors. Gross error detection is conducted by combining data from star trackers and gyroscopes according to their consistency of attitude changes. An observability analysis of gyroscope system errors reveals that the function model is optimal when only the gyroscope bias parameter is set. The adaptive fusion scheme based on the extended Kalman filter (EKF) and least squares (LSQ) methods is developed to improve attitude determination accuracy through reverse filtering smoothing and multiple integral averaging, respectively. The algorithms are tested using real data from the TH-1 satellite. The performance of the scheme is analyzed and evaluated, confirming its accuracy and robustness. Furthermore, the scheme effectively reduces the impact of satellite flutter.