基于可观测度分析和模型预测的空间自标定方法

光纤陀螺以其优点适合空间飞行器定姿要求,但长期在轨飞行必须对光纤陀螺进行自标定。通常采用高维复杂陀螺模型计算实时性误差,而采用舍去状态量的降维模型影响系统定姿精度。针对这一问题,通过星敏感器观测,对高维陀螺模型进行可观测度分析,提出了一种基于模型预测的降维模型,将可观测度较低的状态作为模型误差进行预测估计,保证了系统精度并大大减小了计算量。半物理仿真结果表明此算法精度略高于高维模型自标定方法,计算量仅为高维模型的1/10,显著提高了系统的实时性。

Spacial Gyroscope Calibration Algorithm Based on Obserbability Analysis and Model Predicition

When mobile special maneuver platform working on the long-term tracking,fiber-optic gyroscope can meet the demand in the space due to its advantages.Fiber-optic gyroscope self calibration should be done in the space.High fidelity gyro models cost more computation time and usually reduced module filter lost navigation precision.Aimed at the problem,using sensor observation to analyze the high-dimension gyroscope module and taking the states with low observability as model errors,a reduced-dimension module based on module prediction was proposed.The proposed module could guarantee the precision,decrease the computation and improve the algorithm speed.The hardware-in-the-loop simulation results show that the algorithm has higher precision than that of the high-dimension self calibration algorithm,and its computational cost is only 1/10 of the full-dimension model.The algorithm also improves the real-time performance.