期刊文献+

Optimal and suboptimal white noise smoothers for nonlinear stochastic systems 预览

Optimal and suboptimal white noise smoothers for nonlinear stochastic systems
在线阅读 免费下载
收藏 分享 导出
摘要 A new approach of smoothing the white noise for nonlinear stochastic system was proposed.Through presenting the Gaussian approximation about the white noise posterior smoothing probability density function,an optimal and unifying white noise smoothing framework was firstly derived on the basis of the existing state smoother.The proposed framework was only formal in the sense that it rarely could be directly used in practice since the model nonlinearity resulted in the intractability and infeasibility of analytically computing the smoothing gain.For this reason,a suboptimal and practical white noise smoother,which is called the unscented white noise smoother(UWNS),was further developed by applying unscented transformation to numerically approximate the smoothing gain.Simulation results show the superior performance of the proposed UWNS approach as compared to the existing extended white noise smoother(EWNS) based on the first-order linearization. A new approach of smoothing the white noise for nonlinear stochastic system was proposed. Through presenting the Gaussian approximation about the white noise posterior smoothing probability density fimction, an optimal and unifying white noise smoothing framework was firstly derived on the basis of the existing state smoother. The proposed framework was only formal in the sense that it rarely could be directly used in practice since the model nonlinearity resulted in the intractability and infeasibility of analytically computing the smoothing gain. For this reason, a suboptimal and practical white noise smoother, which is called the unscented white noise smoother (UWNS), was further developed by applying unscented transformation to numerically approximate the smoothing gain. Simulation results show the superior performance of the proposed UWNS approach as compared to the existing extended white noise smoother (EWNS) based on the first-order linearization.
作者 王小旭 潘泉 梁彦 程咏梅 Xiao-xu Wang 王小旭 (11532) ;Quan Pan 潘泉 (11532) ;Yan Liang 梁彦 (11532) ;Yong-mei Cheng 程咏梅 (11532) ;
机构地区 College of Automation
出处 《中南大学学报:英文版》 SCIE EI CAS 2013年第3期655-662,共8页 Journal of Central South University of Technology
基金 Projects(61203234, 61135001, 61075029, 61074179) supported by the National Natural Science Foundation of China Project (20110491692) supported by the Postdoctoral Science Fotmdation of China
关键词 非线性随机系统 噪声平滑 高斯近似 概率密度 数值近似 UT变换 仿真结果 白噪声 nonlinear stochastic system white noise smoother optimal framework unscented transformation
  • 相关文献

参考文献19

  • 1MENDEL J M. White-estimators for seismic data processing in oil exploration [J]. IEEE Transactions on Automatic Control, 1977, 22(5): 694-706. 被引量:1
  • 2DENG Z L, ZHANG H S, LIU S J, ZHOU L. Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems [J], Autmatica, 1996, 32(2): 199-216. 被引量:1
  • 3MENDEL J M. Optimal seismic deconvolution: An estimation based approach [M], New York: Academic Press, 1983:134-138. 被引量:1
  • 4DENG Z L, XU Y. White noise estimation theory based on Kalman filtering [J], ACTA AUTOMATICA SINICA, 2003,29(1): 143-146. 被引量:1
  • 5DENG Z L. Unifying and universal optimal white noise estimators for time-varying systems [J], Control Theory & Application, 2003, 20(1): 143-146. 被引量:1
  • 6SUN S L, GAO Y, DENG Z L. Information fusion white noise deconvolution estimators for time-varying systems [J], Signal Processing, 2008, 88(5): 1233-1247. 被引量:1
  • 7SUN S L, GAO Y, DENG Z L, LI C, WANG J W. Multi-model information fusion Kalman filtering and white noise deconvolution [J], Information Fusion, 2010, 11(2): 163-173. 被引量:1
  • 8RUBAAI A, YOUNG P. EKF-Based PI-/PD-Like fuzzy-neural-network controller for brushless drives [J]. IEEE Transaction on Industry Applications, 2011,47(6): 2391-2401. 被引量:1
  • 9WANG X X, PAN Q, LIANG Y, ZHAO C H. Application of unscented transformation for nonlinear state smoothing [J], ACTA AUTOMATICA SINICA, 2012, 38(7): 1107-1112. 被引量:1
  • 10SIMO S, JOUNI H. On Gaussian optimal smoothing for non-linear state space models [J]. IEEE Transactions on Automatic Control, 2008,55(8): 1938-1941. 被引量:1
投稿分析

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 意见反馈