期刊文献+

基于ZUPT的车载MEMS惯性系统的混合滤波 被引量:5

Combining ZUPT with hybrid particle filter for vehicle MEMS-INS
下载PDF
导出
摘要 针对车载系统在运动过程中运行时间长、动态性能变化频繁、车载导航系统不同的非线性特点,提出通过零速修正方法确定其动态特性。针对动态检测结果提出了混合滤波算法,根据车载导航系统不同的动态特性,特别是在GPS故障的情况下,采用本文提出的滤波方法有效地降低了载体在不同动态特性下的误差影响。实验表明,该方法能有效提高车载系统动态定位,改善由非线性误差导致车载系统误差积累造成的影响。 This paper proposes hybrid unscented particle filter(HUPF) for integrated navigation system (INS) of vehicle which has characteristics of long-time moving and frequent changes in dynamic performance and different non-linear characteristics. The algorithm combined with zero velocity updates method and effectively reduced the error of different vehicle' s dynamic characteristics based on the integrated navigation system of vehicle. The result shows the method effectively improved positioning accuracy and the error accumulation of vehicle.
出处 《电机与控制学报》 EI CSCD 北大核心 2010年第2期31-35,共5页 Electric Machines and Control
基金 国家自然科学基金资助项目(50805004) 西门子中国研究院资助项目(40001014200611)
关键词 混合滤波 惯性导航 非线性 GPS缺失 hybrid unscented particle filter integrated navigation system non-linear GPS output
  • 相关文献

参考文献14

  • 1ANDERSON B O, MOORE J B. Optimal filtering [M]. [ S. l.]:Prentice-Hall, 1979: 32.
  • 2WELCH G,BISHOP G. An introduction to the Kalman filer [ R]. North Carolina: University of North Carolina, 2004.
  • 3JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation [ C ]//Proceedings of the IEEE. Montana : Big Sky, 2004, 92(3) : 401-422.
  • 4WAN E A, MERWE R. The unscented Kalman filter for nonlinear estima-tion [ C ]//Proceedings of International Symposium on Adaptive Systems for Signal Processing Communications and Control Alberta. Canada:[s.n.], 2000, 12(2): 153-158.
  • 5MERWE R, DOUCET A, FREIAS D, et al. The unscented particle filter [ R ]. England : Cambridge University, 2000.
  • 6袁泽剑,郑南宁,贾新春.高斯-厄米特粒子滤波器[J].电子学报,2003,31(7):970-973. 被引量:77
  • 7ARULAMPALAM M S, MASKELL S,GORDON N, et al. A tutorial on particle fihers for on-line nonlinear/non-gaussian bayesian tracking [ C ]//IEEE Transactions on Signal Processing. Hong Kong: University of Hong Kong, 2002, 50(2) : 174 -188.
  • 8DOUCET A. On sequential simulation-based methods for bayesian filtering [ R]. England: University of Cambridge, 1998.
  • 9GUSTAFSSON F. GUNNARSSON F. Particle filters for positioning navigation and tracking[C]//IEEE Transactions on Signal Processing. Oriando, Florida: University of Florida Tampa, 2002, 50(2) : 425 -437.
  • 10RICJARD K. Particle filtering for positioning and tracking applications [ C ] //IEEE Final Rersion for Transactions on Signal Processing. Sweden : Linkopings Universitet, 2005.

二级参考文献11

  • 1南京大学数学系编.数值逼近方法[M].北京:科学出版社,1978..
  • 2G Kitagawa. Monte Carlo filter and smoother for non Gaussian nonlinear state space models [J] .Journal of Computational and Graphical Statistics, 1996,5:1 - 25.
  • 3Avitzour. A stochastic simulation Bayesian approach to multitarget tracking [A] .IEE Proceedings on Radar,Sonar and Navigation [C].UK: lEE, 1995.
  • 4M lsard, Blake. Contour tracking by stochastic propagation of conditional density [ A ]. European Conference on Computer Vision [ C ]. UK:Cambridge, 1996. 343 - 356.
  • 5I Kazuftmfi, K-Q Xiong. Gaussian filters for nonlinear filtering problems[ EB/OL]. available from http://www, researchindex, com.
  • 6S J Julier,J K Uhlmann. A new extension of the Kalman filter to nonlinear systems [ A ]. Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defence Sensing, Sinmlation and Controls[ C], Florida: ISADSSC, 1997.
  • 7A Doucet. On Sequential Simtdafion-Based Methods for Bayesian Filtering [ EB/OL]. available from http://www, researchindex, com.
  • 8R Van der Merwe. A Doucet the Unscented Particle Filter, Advances in Neural Information Processing Systems [M]. M IT,2000.
  • 9N J Gordon, D J Salmond, A F M Smith. A novel approach to nonlinear and non-Ganssian Bayesian state estimation [ A ]. IEE Proceedings-F[C]. UK: IEE, 1993,.
  • 10高钟毓,王进,董景新,赵长德.惯性测量系统零速修正的几种估计方法[J].中国惯性技术学报,1995,3(2):24-29. 被引量:41

共引文献116

同被引文献36

引证文献5

二级引证文献19

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