期刊文献+

基于改进型两步卡尔曼滤波的相对导航方法 被引量:4

Spacecraft Relative Navigation Based on Improved Two Step Kalman Filtering
下载PDF
导出
摘要 采用Clohessy-Wiltshire(C-W)方程描述的近圆轨道相对导航状态方程具有线性的形式,而以航天器相对距离和相对方位作为测量信息的观测方程是非线性的,针对近圆轨道航天器相对导航的这一特点,给出了采用两步卡尔曼滤波(Two Step Kalman Filte-ring,TSF)的相对导航算法,并且利用Unscented变换方法,解决了两步卡尔曼滤波的状态初值确定问题,给出了TSF的完整算法。数值仿真比较了TSF和扩展卡尔曼滤波(Ex-tended Kalman Filtering,EKF)、无迹卡尔曼滤波(Unscented Kalman Filtering,UKF)等算法的性能,验证了采用TSF方法实现相对导航的可行性和有效性。 For the spacecraft relative navigation in the circular orbits,the linear Clohessy-Wiltshire(C-W) equations were used as the state equations,and the nonlinear equations for the relative distance and azimuth between two spacecraft were used as the observation equations.The improved two-step Kalman filtering(TSF) was designed for the special relative navigation problem.The unscented transformation is used for determining the initial distribution about the state vector,and the intact form of the two step Kalman filtering was obtained.The simulation results validate the availability of this navigation method,and show that the TSF can provide better performance than extended Kalman filtering(EKF) and unscented Kalman filtering(UKF).
出处 《中国空间科学技术》 EI CSCD 北大核心 2011年第3期20-25,34,共7页 Chinese Space Science and Technology
关键词 相对导航 观测方程 两步卡尔曼滤波 航天器 Relative navigation Observation equation Two step Kalman filtering Spacecraft
  • 相关文献

参考文献5

  • 1GOODMAN J L, BRAZZEL J P, Chart D A. Challenges of orion rendezvous development [C]. AIAA-2007- 6682, 2007.
  • 2HAUPT G T, KASDIN N J, KEISER G M, et al. An optimal recursive iterative algorithm for discrete nonlin- ear least- squares estimation [J]. Journal of Guidance, Control, and Dynamics, 1996, 19 (3) : 643-649.
  • 3WAN E A, VAN DER MERWE R. The unscented kalman filter for nonlinear estimation [C] //Proceedings of The IEEE Symposium 2000 (AS SPCC). Lake Louise, Alberta, Canada, 2000.. 153-158.
  • 4肖业伦.航天器飞行动力学原理[M].北京:宇航出版社,1995..
  • 5JUI.IER S J, UHLMANN J K, DURRANT WHYTE H F. A new approach for filtering nonlinear systems [C] //Proceedings of the American Control Conference. Seattle, Washington, 1995: 1628-1632.

共引文献66

同被引文献51

  • 1文成林,吕冰,葛泉波.一种基于分步式滤波的数据融合算法[J].电子学报,2004,32(8):1264-1267. 被引量:31
  • 2何英姿,谌颖,韩冬.基于交会雷达测量的相对导航滤波器[J].航天控制,2004,22(6):17-20. 被引量:12
  • 3王玲,邵金鑫,万建伟.基于相对观测量的多机器人定位[J].国防科技大学学报,2006,28(2):67-72. 被引量:12
  • 4范小军,刘锋.一种新的机动目标跟踪的多模型算法[J].电子与信息学报,2007,29(3):532-535. 被引量:32
  • 5高社生,何鹏举,杨波,等.组合导航原理及应用[M].西安:西北工业大学出版社,2012.
  • 6Rudolph van der Merwe.Sigma-point Kalman filters for probabilistic inference in dynamic state-space models[D].Portland:School of Science & Engineering at Oregon Health & Science University,2004.
  • 7Kalman R E.A new approach to linear filtering and prediction problems[J].Transactions of the ASME-Journal of Basic Engineering,1960,82:35.
  • 8Chen Jinguang,Li Jie,Gao Xinbo.Improved quadrature Kalman filter with large number[J].Journal of Information & Computational Science,2010,7(5):1097.
  • 9Arulampalam M S,Maskell S.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174.
  • 10Phanenf R J.Approximate nonlinear estimation[D].Cambridge:MIT,1968.

引证文献4

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部