基于多模型自适应估计的姿态敏感器误差校准  

MULTIPLE-MODEL ADAPTIVE ESTIMATOR FOR ATTITUDE SENSOR CALIBRATION

作　　者：熊凯[1] 魏春岭[2] 刘良栋[1] 

机构地区：[1]北京控制工程研究所,北京100190 [2]空间智能控制技术重点实验室,北京100190

出　　处：《系统科学与数学》2015年第7期745-756,共12页Journal of Systems Science and Mathematical Sciences

基　　金：国家973计划(2013CB733100);重点实验室基金(9140C590201130C59213)资助课题

摘　　要：提出一种基于多模型自适应估计(MMAE)的星敏感器低频误差(LFE)校准方法.星敏感器低频误差主要是由空间热环境的周期性变化造成的,会对卫星姿态确定精度造成显著影响.低频误差的影响可以通过扩维卡尔曼滤波(AKF)进行校准.但是,在星敏感器观测量中不存在低频误差的情况下,AKF的姿态估计精度往往不及传统卡尔曼滤波(KF).针对这一问题,将KF与AKF相结合,设计了基于MMAE的姿态确定滤波算法,该算法能够根据星敏感器在轨误差特性自适应的选择KF或AKF算法进行滤波.仿真结果表明,所提算法综合性能优于KF和AKF,适用于对姿态确定精度要求较高的高分辨率对地观测卫星.This paper presents a multiple-model adaptive estimator （MMAE） to calibrate the star sensor low frequency error （LFE）. The star sensor LFE, which is caused primarily by the periodic thermal distortion, has a great impact on satellite attitude determination accuracy. The unfavorable effect of the LFE can be partly eliminated by using the calibration algorithm based on the augmented Kalman filter （AKF）. However, the AKF may be worse than the traditional Kalman filter （KF） in the absence of the LFE. In order to cope with this problem, the MMAE is applied first time for combining the AKF and the KF in the spacecraft attitude determination system, such that satisfactory performance can be achieved in different operating scenarios. It is shown via numerical studies that the presented algorithm outperforms the AKF and the KF. The calibration algorithm is applicable for satellite attitude determination.

关 键 词：多模型自适应估计 扩维卡尔曼滤波 卫星姿态确定 误差校准. 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]