基于纵向滤波的星敏感器低频误差在线估计

叶立军; 刘付成; 尹海宁; 徐樱; 宝音贺西

doi:10.7527/S1000-6893.2019.23163

航空学报 >

2019 , Vol. 40 >Issue 10: 323163 - 323163

DOI: https://doi.org/10.7527/S1000-6893.2019.23163

电子电气工程与控制

基于纵向滤波的星敏感器低频误差在线估计

叶立军 ,
刘付成 ,
尹海宁 ,
徐樱 ,
宝音贺西

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1. 清华大学航天航空学院, 北京 100084;
2. 上海航天控制技术研究所, 上海 201109;
3. 上海市空间智能控制技术重点实验室, 上海 201109

收稿日期: 2019-05-16

修回日期: 2019-07-10

网络出版日期: 2019-08-12

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

YE Lijun ,
LIU Fucheng ,
YIN Haining ,
XU Ying ,
BAOYIN Hexi

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1. School of Aerospace Engineering, Tsinghua University, Beijing 10084, China;
2. Shanghai Aerospace Control Technology Institute, Shanghai 201109, China;
3. Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai 201109, China

Received date: 2019-05-16

Revised date: 2019-07-10

Online published: 2019-08-12

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摘要

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。

关键词： 低频误差; 轨道周期; 纵向滤波; 在线估计; 星敏感器; 数据融合

本文引用格式

叶立军 , 刘付成 , 尹海宁 , 徐樱 , 宝音贺西 . 基于纵向滤波的星敏感器低频误差在线估计[J]. 航空学报, 2019 , 40(10) : 323163 -323163 . DOI: 10.7527/S1000-6893.2019.23163

Abstract

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.

Key words： low frequency error; orbital period; vertical filter; online estimation; star tracker; data fusion

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