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 引用本文: 李英民, 董银峰, 赖明. 地震动瞬时谱估计的UnscentedKalman滤波方法[J]. 应用数学和力学, 2007, 28(11): 1370-1378.
LI Ying-min, DONG Yin-feng, LAI Ming. Instantaneous Spectrum Estimation of Earthquake Ground Motions Based on Unscented Kalman Filter Method[J]. Applied Mathematics and Mechanics, 2007, 28(11): 1370-1378.
 Citation: LI Ying-min, DONG Yin-feng, LAI Ming. Instantaneous Spectrum Estimation of Earthquake Ground Motions Based on Unscented Kalman Filter Method[J]. Applied Mathematics and Mechanics, 2007, 28(11): 1370-1378.

地震动瞬时谱估计的UnscentedKalman滤波方法

作者简介:李英民(1968- ),男,山东人,教授,博士生导师(联系人.Tel:+86-23-65121991;E-mail:liyingmin@cqu.edu.cn).
• 中图分类号: O211.64；P315.3

Instantaneous Spectrum Estimation of Earthquake Ground Motions Based on Unscented Kalman Filter Method

• 摘要: 用时变ARMA模型描述地震动时程，提出了采用Unscented Kalman滤波技术实现地震动瞬时谱估计的思路．算例分析表明，Unscented Kalman滤波方法较Kalman滤波方法适用范围广，具有较高的时间和频率分辨率，能够更好地跟踪地震动的局部特性，适合处理非线性模型或有突变特性的模型的辨识问题．不同阶数ARMA模型的估计结果还表明，以往被忽略的ARMA模型的理论频率分辨力对地震动瞬时谱估计精度有重要影响，应作为一个参考指标在ARMA模型的判阶中加以考虑．
•  [1] 李英民.工程地震动的模型化研究[D].博士学位论文.重庆:重庆建筑大学,1999. [2] Conte J P, Pister K S, Mahin S A. Influence of the earthquake ground motion process and structural properties on response characteristics of simple structures[R]. Report No. UCB/EERC-90/09, Earthquake Engineering Research Center, University of California, Berkeley, CA, 1990. [3] Cohen L. Time-frequency distributions—a review[J].Proceedings of the IEEE,1989,77(7):941-981. doi: 10.1109/5.30749 [4] Huang N E, Shen Z, Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proc of the Royal Society of London, Ser A,1998,454(1971): 903-995. [5] 谢衷洁. 时间序列分析[M].北京: 北京大学出版社, 1990. [6] Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, Transactions of the ASME, Ser D,1960,82(1):35-45. [7] Sorenson H W. Least-square estimation: from Gauss to Kalman[J].IEEE Spectrum,1970,7(7):63-68. [8] Ljung L.System Identification—Theory for the User[M].2nd Ed.New Jersey:PTR Prentice Hall, 1999. [9] Haykin S.Kalman Filtering and Neural Networks[M].New York: John Wiley and Sons Inc,2001. [10] Sanjeev A, Simon M, Neil G,et al.A tutorial on particle filters for on-line no-linear/non-Gaussian Bayesian tracking[J].IEEE Trans on Signal Processing,2002,50(2):174-188. [11] Julier S, Uhlmann J K. A new extension of the Kalman filter to nonlinear system[A].In: Proc of AeroSence:The 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls[C].Orlando,Florida:SPIE，1997,182-193. [12] Parzen E. Some recent advances in time series modeling[J].IEEE Trans on Automatic Control,1974,19(6):723-730. [13] Jong P, Penzer J. The ARMA model in state space form[J].Journal of Statistics & Probability Letters,2004,70(1):119-125. [14] Akaike H. A new look at the statistical model identification[J].IEEE Trans on Automatic Control,1974,19(6):716-723. [15] Broersen P M T. Finite sample criteria for autoregressive order selection[J].IEEE Trans on Signal Processing,2000,48(12):3550-3558. [16] 康宜华, 何岭松, 杨叔子. ARMA谱的理论频率分辨力[J]. 华中理工大学学报,1995,23(6):101-104. [17] Gustaffasson F, Gunnarsson S, Ljung L. Shaping frequency-dependent time resolution when estimation spectral properties with parametric methods[J].IEEE Trans on Signal Processing,1997,45(4):160-163. [18] Dong Y F, Zhen N N, Lai M,et al.Estimation of instantaneous spectrum of earthquake ground motions using neural networks[A].In:Andcrson E DL Ed.Proc the 13th World Conference on Earthquake Engineering[C].Vancouver,Canada:Mira Digital Publishing,2004, PN1446.

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出版历程
• 收稿日期:  2006-09-20
• 修回日期:  2007-09-04
• 刊出日期:  2007-11-15

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