全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

有限模型卡尔曼滤波的框架及一个实例:MVDP-FMKF算法

DOI: 10.3724/SP.J.1004.2013.01246, PP. 1246-1256

Keywords: 有限模型卡尔曼滤波,最小矢量距离准则,自适应切换,MEMS陀螺降噪

Full-Text   Cite this paper   Add to My Lib

Abstract:

?卡尔曼滤波技术在很多领域已得到广泛的应用,标准卡尔曼滤波算法是基于线性高斯系统模型假设,需要已知精确的系统模型.当系统模型存在较大不确定性时,运用基于不精确系统模型设计的卡尔曼滤波算法时,滤波效果通常不能满足系统需求甚至发散.在很多实际应用中,往往需要大量工作才能得到较为精确的系统模型或者基本不可能给出精确的系统模型.为解决这一具有工程实践意义的问题,受有限模型自适应控制思想的启发,本文介绍了一个有限模型卡尔曼滤波算法的框架.在该框架中,假设系统模型的不确定性可由有限个已知模型的集合(模型个数不限)来刻划或近似,从而先验未知的系统模型可以通过充分挖掘系统运行的动态后验数据中的信息,利用已知模型集在线自适应估计以逼近真实系统的模型,最终实现模型有较大不确定性时仍能有效滤波.在此框架下,基于最小化距离向量的准则,我们引入一种模型自适应切换的算法(MVDP-FMKF),给出其数学描述,并通过仿真研究及MEMS陀螺漂移测试验证了算法的有效性.本工作展现了有限模型卡尔曼滤波的机制在导航系统等应用中具有有效性、实用性.

References

[1]  Feng B, Li Q, Yang P P, Fu G D. On-line realization of multi-scale soft-threshold de-noising of MEMS gyro by wavelet. In: Proceedings of the 9th International Conference on Electronic Measurement and Instruments. Beijing, China: IEEE, 2009. 442-445
[2]  Deng Z L, Sun S L. Wiener state estimators based on Kalman filtering. Acta Automatica Sinica, 2004, 30(1): 126-130
[3]  Kluge S, Reif K, Brokate M. Stochastic stability of the extended Kalman filter with intermittent observations. IEEE Transactions on Automatic Control, 2010, 55(2): 514-518
[4]  Carlson N A. Federated square root filter for decentralized parallel processors. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(3): 517-525
[5]  Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3): 401-422
[6]  Karasalo M, Hu X M. An optimization approach to adaptive Kalman filtering. Automatica, 2011, 47(8): 1785-1793
[7]  Ma H B. Finite-model adaptive control using an LS-like algorithm. International Journal of Adaptive Control and Signal Processing, 2006, 21(5): 391-414
[8]  Ma H B. Several algorithms for finite-model adaptive control. Mathematics of Control, Signals, and Systems, 2008, 20(3): 271-303
[9]  Julier S J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems. In: Proceedings of the 1997 International Society for Optical Engineering. Orlando, FL, USA: SPIE-International Society for Optical Engineering, 1997. 182-193
[10]  Xie L H, Soh Y C. Robust Kalman filtering for uncertain systems. Systems and Control Letters, 1994, 22(2): 123-129
[11]  Li X R, Jilkov V P. Survey of maneuvering target tracking-part V: Multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255-1321
[12]  Zhao L, He Z F. An in-coordinate interval adaptive Kalman filtering algorithm for INS/GPS/SMNS. In: Proceedings of the 10th IEEE International Conference on Industrial Informatics. Beijing, China: IEEE, 2012. 41-44
[13]  Kalman R E. A new approach to linear filtering and prediction problems. Transactions of the ASME—Journal of Basic Engineering, 1960, 82: 35-45
[14]  Koh B S, Junkins J L. Kalman filter for linear fractional order systems. Journal of Guidance, Control, and Dynamics, 2012, 35(6): 1816-1827
[15]  Ge Quan-Bo, Li Wen-Bin, Sun Ruo-Yu, Xu Zi. Research on centralized fusion algorithms based on EKF for multisensor non-linear systems. Acta Automatica Sinica, 2013, 39(6): 816-825 (in Chinese)
[16]  Zhao L, Wang X X, Sun M, Ding J C, Yan C. Adaptive UKF filtering algorithm based on maximum a posterior estimation and exponential weighting. Acta Automatica Sinica, 2010, 36(7): 1007-1019 (in Chinese)
[17]  Kai X, Wei C L, Liu L D. Robust extended Colman filtering for nonlinear systems with stochastic uncertainties. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2010, 40(2): 399-405
[18]  Yeste Ojeda O A, Grajal J. Adaptive-FRESH filters for compensation of cycle-frequency errors. IEEE Transactions on Signal Processing, 2010, 58(1): 1-10
[19]  Ma H B. Finite-model adaptive control using WLS-like algorithm. Automatica, 2007, 43(4): 677-684
[20]  Xiao X, Feng B, Wang B. On-line realization of SVM Kalman filter for MEMS gyro. In: Proceedings of the 3rd International Conference on Measuring Technology and Mechatronics Automation. Shanghai, China: IEEE Computer Society, 2011. 768-770
[21]  Chen T S, Schon T B, Ohlsson H, Ljung L. Decentralized particle filter with arbitrary state decomposition. IEEE Transactions on Signal Processing, 2011, 59(2): 465-78
[22]  Ma Hong-Bin. Capability and Limitation of Feedback Mechanism in Dealing with Uncertainties of Some Discrete-time Control Systems[Ph.D. dissertation], Graduate School of Chinese Academy of Sciences, China, 2006 (in Chinese)
[23]  Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. New York: John Wiley and Sons, 2001

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133