期刊文献+

快速次优固定区间Wiener平滑器算法 被引量:2

Fast suboptimal fixed-interval Wiener smoothing algorithm
下载PDF
导出
摘要 为了克服带相关噪声控制系统的最优固定区间Kalman平滑算法要求较大计算负担的缺点,应用Kalman滤波方法,基于CARMA新息模型,由稳态最优Kalman平滑器导出了带相关噪声控制系统的最优固定区间Wiener递推状态平滑器,它带有系数阵指数衰减到零的高阶多项式矩阵.用截断系数矩阵近似为零的项的方法提出了相应的快速次优固定区间Wiener平滑算法.它显著地减少了计算负担,便于实时应用,还给出了截断误差公式和选择截断指标的公式.仿真例子说明了快速平滑算法的有效性. In order to overcome the drawback that the optimal fixed-interval Kalman smoothing algorithms require a large computational burden for control systems with correlated noise, by using the Kalman filtering method based on the controlled au-toregressive moving average (CARMA) innovation model, the optimal fixed-interval Wiener recursive state smoother is derived from the steady-state optimal Kalman smoother. The obtained smoother contains the high-order polynomial matrix whose coefficient matrices exponentially decay to zero. A fast sub-optimal fixed-interval Wiener smoothing algorithm is proposed by means of truncating terms with coefficient matrices approximately equal to zero. Thus, the computational burden is obviously reduced and the method is suitable for real time application. Both the formula for truncation error and the formula for selecting truncation index are given. A simulation example shows the effectiveness of the proposed algorithm.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2004年第2期275-278,共4页 Control Theory & Applications
基金 国家自然科学基金项目(60374026) 黑龙江省自然科学基金项目(F01-15).
关键词 状态估计 固定区间平滑 Wiener状态平滑器 快速次优平滑算法 Kalman滤波方法 state estimation fixed-interval smoothing Wiener state smoother fast suboptimal smoothing algorithm Kalman filtering method
  • 相关文献

参考文献2

  • 1KAILATH T, SAYYED A H, HASSIBI B. Linear Estimation [M].Englewood Cliffs, NJ: Printice-Hall, 2000.
  • 2ANDERSON B D, MOORE J B. Optimal Filtering [M]. Englewood Cliffs, NJ: Prinfice-Hall, 1979.

同被引文献11

  • 1安德玺,梁彦,周东华.一种基于滤波参数在线辨识的鲁棒自适应滤波器[J].自动化学报,2004,30(4):560-566. 被引量:7
  • 2MIRKIN L, TADMDR G. Fixed-lag smoothing as a constrained version of the fixed-interval case [C]// Proceedings of the 2004 American Control Conference. Boston, Massachusetts, USA: American Automatic Control Council, 2004: 4165-4170.
  • 3DONALD C F, JAMES E P. The optimum linear smoother as a combination of two optimum linear filters[J]. IEEE Transactions on Automatic Control, 1969, 14(8):387-390.
  • 4SUN Shuli, MA Jing. Optimal filtering and smoothing for discrete-time stochastic singular systems [J]. Signal Processing, 2007, 87 (1):189-201.
  • 5NAKAMORI S, HERMOSO-CARAZO A, LIN ARES-PEREZ J. A general smoothing equation for signal estimation using randomly delayed observations in the correlated signal-noise case [J]. Digital Signal Processing, 2006 (16) : 369-388.
  • 6HERMOSO-CARAZO A, LINARES-PEREZ J. Linear smoothing for discrete-time systems in the presence of correlated disturbances and uncertain observations [J]. IEEE Transactions on Automatic Control, 1995, 40(8) : 1486-1488.
  • 7LI Xiaorong, ZHU Yunmin, WANG Jie, et al. Opti mal linear estimation fusion, part Ⅰ: unified fusion rules [J]. IEEE Transactions on Information Theory, 2003, 49(9): 2192-2208.
  • 8孙书利,邓自立.极点配置固定区间Kalman平滑器和Wiener平滑器(英文)[J]自动化学报,2004(02).
  • 9韩崇昭,王洁,李晓榕.一般相关量测噪声下线性系统的平滑估计算法[J].西安交通大学学报,2000,34(9):1-4. 被引量:4
  • 10左东广,韩崇昭,魏瑞轩,郑林.相关噪声情况下航迹的关联及融合算法[J].电子学报,2002,30(8):1117-1120. 被引量:12

引证文献2

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