期刊文献+

基于UKF的低成本SINS/GPS组合导航系统滤波算法 被引量:17

Nonlinear algorithm based on UKF for low-cost SINS/GPS integrated navigation system
下载PDF
导出
摘要 针对MIMU的精度不高,会带来较大的初始对准误差角,如果继续采用传统的小干扰线性方程就会给滤波带来很大误差,甚至发散。针对这个问题,对低成本SINS/GPS组合导航系统建立了基于四元数误差模型的非线性滤波方程,并采用了UKF非线性滤波方法。针对四元数误差模型单纯使用UKF方法无法估计加计零偏和陀螺漂移的问题,提出将UKF和EKF相结合的算法,仿真结果表明,比起扩展卡尔曼滤波以及采用传统小干扰线性方程的卡尔曼滤波,这种方法能够提高姿态误差角特别是方位误差角的估计精度。 Aimed at a low-cost scheme of SINS/GPS integrated navigation system using low precision inertial sensors (MIMU), the precise error equations is basic for filter. The psi-angle model has been widely used. However, the model is not effective for a system with large attitude errors because the neglected error terms in the model degrade the performance of a designed filter. This paper establishes nonlinear mathematics model based on quaternion error equations. For essential nonlinear systems, the unscented Kalman filter (UKF) has some advantages such as high estimation precision, fast convergence, and so on. In order to deal with the problem that the inertial sensors errors can not be estimated using UKF method, an improved algorithm combining UKF and extended Kalman filters is proposed. The simulation results show that this method has better precision of all misalignment angles especially the azimuth one than that of extended Kalman filters.
出处 《系统工程与电子技术》 EI CSCD 北大核心 2007年第3期408-411,共4页 Systems Engineering and Electronics
基金 国防基础科研项目基金资助课题(K1204060116)
关键词 组合导航 导航误差 非线性 滤波算法 integrated navigation navigation error nonlinear filter algorithm
  • 相关文献

参考文献5

  • 1Soehren W,Schipper B,Lund C.A MEMS-based guidance,navigation,and control unit[C]// Position Location and Navigation Symposium,IEEE,2002(4):189-195.
  • 2Julier S,Uhlmann J,Hugh F.A new method for the nonlinar transformation of means and covariances in filters and estimators[J].IEEE Trans.on Automatic Control,2000,45 (3):477-482.
  • 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
  • 4Yu M J,Lee J G,Park H W.Comparison of SDINS in-flight alignment using equivalent error models[J].IEEE Trans.on Aerospace and Electronic Systems,1999,35 (3):1046-1054.
  • 5Bilal Akin,Umut Orguner,Aydin Ersak.State estimation of induction motor using unscented Kalman filter[C]//Proc.of IEEE Conference on Control Applications,CCA,2003(2):915-919.

二级参考文献6

共引文献9

同被引文献120

引证文献17

二级引证文献74

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