电子与控制

基于自适应容积卡尔曼滤波的非合作航天器相对运动估计

  • 于浛 ,
  • 魏喜庆 ,
  • 宋申民 ,
  • 刘铭
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  • 1. 哈尔滨工业大学 控制理论与制导技术研究中心, 黑龙江 哈尔滨 150001;
    2. 上海机电工程研究所, 上海 201109
于浛男,博士研究生。主要研究方向:航天器姿态确定,非线性滤波以及视觉/惯性组合导航。E-mail:yuhanihit@163.com;宋申民男,博士,教授,博士生导师。主要研究方向:非线性系统与复杂系统的鲁棒控制与智能控制,启发式智能优化方法及其应用研究,飞行器控制、制导与仿真研究,以及先进滤波方法及其在飞行器导航中的应用。Tel:0451-86402224-8214,E-mail:songshenmin@hit.edu.cn

收稿日期: 2013-10-25

  修回日期: 2014-03-20

  网络出版日期: 2014-04-04

基金资助

国家自然科学基金(61174037);国家“973”计划(2012CB821205)

Relative Motion Estimation of Non-cooperative Spacecraft Based on Adaptive CKF

  • YU Han ,
  • WEI Xiqing ,
  • SONG Shenmin ,
  • LIU Ming
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  • 1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China;
    2. Shanghai Electro-Mechanical Engineering Institute, Shanghai 201109, China

Received date: 2013-10-25

  Revised date: 2014-03-20

  Online published: 2014-04-04

Supported by

National Natural Science Foundation of China (61174037);National Basic Research Program of China (2012CB821205)

摘要

针对目标星无信息传递以及无特征光点的非合作目标交会对接问题,提出了一种利用立体视觉的相对运动估计方法。由于测量装置安装在非质心位置时,CW方程描述的卫星相对运动所存在的误差不能忽略,因而通过给出适用于椭圆轨道和圆轨道下的一般耦合模型,来描述姿态运动对位置运动的影响。为了克服模型的严重非线性以及噪声统计特性时变的问题,提出了基于Sage-Husa噪声估计器的自适应滤波器。仿真表明:该算法能够适应测量噪声统计特性随时间变化的情况,具有较高的相对运动估计精度。

本文引用格式

于浛 , 魏喜庆 , 宋申民 , 刘铭 . 基于自适应容积卡尔曼滤波的非合作航天器相对运动估计[J]. 航空学报, 2014 , 35(8) : 2251 -2260 . DOI: 10.7527/S1000-6893.2014.0026

Abstract

A relative state estimation method is proposed based on stereo vision for non-cooperative satellite with no communication and information about structure. Since the error of clohessy wiltshire (CW) model can not be neglected when camera does not coincide with the center of mass of spacecraft, a general kinematic coupling which is appropriate for both eccentric orbit and circular orbit is used to describe the relationship between rotation and translation dynamics. Adaptive cubature Kalman filter combing Sage-Husa noise statistic estimator is introduced for nonlinear filtering problem with time-varying noise. Simulation results show that the method can be able to adapt the time-varying noise and its precision for relative motion estimation is higher.

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