期刊文献+

基于自适应UKF算法的机载INS/GPS空中对准研究 被引量:9

In-flight alignment research for airborne INS/GPS based on adaptive unscented Kalman filter algorithm
下载PDF
导出
摘要 在空中对准失准角不满足小角度假设的条件下,推导了一种新的机载INS/GPS大失准角空中对准的误差模型。将基于极大似然估计的自适应估计器与无迹卡尔曼滤波(unscented Kalman filter,UKF)算法相结合,修改自适应滤波算法中自适应参数的表达式。提出将自适应UKF算法用于非线性误差模型的空中对准方案中。仿真表明,自适应UKF算法能够克服噪声统计模型不准确对滤波结果的影响,失准角估计的精度好于UKF算法的精度。 If there is no small misalignment of in-flight alignment any more,an error model of in-flight alignment is presented under the condition of large misalignment of airborne INS/GPS.An unscented Kalman filter(UKF) algorithm is developed combined with adaptive estimator based on the maximum likelihood estimation criterion.The simulation shows that the estimation of adaptive UKF is not degraded by the inaccurate statistics characteristic of stochastic information,and has a better performance than that of an unscented Kalman filter.
作者 周峰 孟秀云
出处 《系统工程与电子技术》 EI CSCD 北大核心 2010年第2期367-371,共5页 Systems Engineering and Electronics
关键词 空中对准 自适应估计 无迹卡尔曼滤波 in-flight alignment adaptive estimation unscented Kalman filter
  • 相关文献

参考文献10

二级参考文献29

  • 1王丹力,张洪钺.惯导系统初始对准的非线性滤波算法[J].中国惯性技术学报,1999,7(3):18-22. 被引量:30
  • 2ZhangHongmei DengZhenglong.UKF-based attitude determination method for gyroless satellite[J].Journal of Systems Engineering and Electronics,2004,15(2):105-109. 被引量:7
  • 3张红梅,邓正隆.UKF方法在陆地车辆组合导航中的应用[J].中国惯性技术学报,2004,12(4):20-23. 被引量:10
  • 4Brady T, Tillier C, Brown R, Jimenez A, Kourepenis A. The inertial stellar compass: a new direction in spacecraft attitude determination[C]. In: SSC- Ⅱ-1.
  • 5BAR-ITZHACK I Y, RE1NER J. Recursive attitude determination from vector observation: direction cosine matrix identification[J].Journal of Guidance, Control, and Dynamics, 1984, 7(1) :51 - 56.
  • 6OSHMAN Y, MARKLEY F L. Minimal-parameter attitude matrix estimation from vector observation [ J ]. Journal of Guidance, Control,and Dynamics, 1998, 21 (4) :595 - 602.
  • 7OSHMAN Y, MARKLEY F L. Sequential attitude and attitude-rate estimation using integrated parameters [ J ]. Journal of Guidance,Control, and Dynamics, 1999, 21(3): 385- 394.
  • 8Julier S, Uhlmann J, Hugh F. A new method for the nonlinear transfommtion of means and covariances in filters and estimators[J]. IEEE Transactions on Automatic Control, 2000, 45 (3) :477 - 482.
  • 9Julier S, Uhlmann J K. A general method for approximating nonlinear transformations of probability distributions[R].Robotics Research Group, Department of Engineering Science, University of Oxford, 1994.
  • 10Julier S, Uhlmann J K, etc. A new method for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Trans. A. C., 2000, 45(3): 477-482.

共引文献41

同被引文献75

引证文献9

二级引证文献27

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