基于纵向滤波的星敏感器低频误差在线估计

多敏感器数据融合是获得更高精度姿态测量的有效方法，敏感器数据融合前必须先修正低频误差。首先，介绍了星敏感器低频误差（LFE）的产生机理及对其在线估计的必要性。其次，针对传统算法的不足，提出了基于纵向滤波的低频误差在线估计算法，该算法将传统低频误差估计问题转化为若干个常值误差估计问题，提高了估计精度。最后，给出了该算法具体实施方式，说明相关参数物理意义及选取原则。通过理论分析及仿真，算法误差可忽略不计。通过在轨数据仿真，星敏感器轨道周期低频误差可被消除。

Online estimation of low frequency error for star tracker based on vertical filter

It's an efficient way to enhance the attitude measurement accuracy by data fusion of multi-sensors, and the sensor's Low Frequency Error (LFE) should be calibrated before data fusion. First, the generating mechanism of the star tracker's LFE is introduced, and the reason why the LFE should be estimated online is explained. Second, considering the deficiency of the traditional algorithm in LFE estimation, the way of LFE estimation based on vertical filter is proposed. The proposed method turns the estimation of LFE into several constant errors, improving the accuracy of the estimation. Third, the detailed operation steps are given, and the physical significance and the se-lection principle of related parameters are explained. Theoretical analyses and simulations show that the error of this algorithm could be ignored. In-orbit data simulation shows that the orbital period LFE of the star tracker could be eliminated.