利用分步Kalman滤波器的自主定轨信息融合算法

将星敏感器/红外地平仪的天文定轨信息与星间观测进行信息融合是消除基于星间链路自主定轨中星座整体旋转问题的有效途径。针对两种定轨方法精度相差较大导致融合效果较差的问题，提出了一种分步 Kalman 滤波算法。该算法利用星敏感器观测信息能够有效修正旋转参数的特点，将星敏感器观测信息和星间观测信息进行分步处理和最优信息融合以消除自主定轨算法中存在的星座整体旋转误差，提高定轨精度。通过对Walker星座的仿真表明，利用提出的分步Kalman滤波信息融合算法，星座自主定轨60天后星座URE误差能够稳定在 1.5米 以内，且能有效消除星座整体旋转误差。

An Information Fusion Algorithm of Autonomous Orbit Determination Based-on Step Kalman Filter

The information fusion of the star sensor/infrared horizon sensor observation and the inter satellite measurements in autonomous orbit determination is an effective method to solve the constellation rotation problem. In this paper, a step Kalman filter algorithm is proposed to solve the fusion problem caused by the different accuracy of the two orbit determination methods. The star sensor observation information and the range measurement information are processed step by step in the information fusion algorithm. The results of simulation to Walker constellation show that the constellation URE of autonomous orbit determination will be within 1.5 meters in 60 days and not be increasing over time by using the fusion algorithm. The fusion algorithm will be effective to correct the constellation rotation error.