2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

有限时间一致无迹Kalman滤波器

刘鹏 田玉平 张亚

刘鹏, 田玉平, 张亚. 有限时间一致无迹Kalman滤波器. 自动化学报, 2020, 46(7): 1357-1366. doi: 10.16383/j.aas.2018.c170726
引用本文: 刘鹏, 田玉平, 张亚. 有限时间一致无迹Kalman滤波器. 自动化学报, 2020, 46(7): 1357-1366. doi: 10.16383/j.aas.2018.c170726
LIU Peng, TIAN Yu-Ping, ZHANG Ya. Finite-time Consensus Based Unscented Kalman Filter. ACTA AUTOMATICA SINICA, 2020, 46(7): 1357-1366. doi: 10.16383/j.aas.2018.c170726
Citation: LIU Peng, TIAN Yu-Ping, ZHANG Ya. Finite-time Consensus Based Unscented Kalman Filter. ACTA AUTOMATICA SINICA, 2020, 46(7): 1357-1366. doi: 10.16383/j.aas.2018.c170726

有限时间一致无迹Kalman滤波器

doi: 10.16383/j.aas.2018.c170726
基金项目: 

国家自然科学基金 61573105

国家自然科学基金 61473081

江苏省自然科学基金 BK20141341

详细信息
    作者简介:

    刘鹏  东南大学自动化学院博士研究生. 2006年获得河南工业大学理学院学士学位, 2011年获得温州大学数学与信息科学学院硕士学位.主要研究方向为多智能体系统, 结构系统, 分布式估计. E-mail: PengLiu_SEU@163.com

    张亚  东南大学自动化学院副教授.主要研究方向为多智能体系统, 分布式滤波理论. E-mail: yazhang@seu.edu.cn

    通讯作者:

    田玉平  东南大学自动化学院教授.主要研究方向为多智能体系统, 通信网络中的优化与控制.本文通信作者. E-mail: yptian@seu.edu.cn

  • 本文责任编委 诸兵

Finite-time Consensus Based Unscented Kalman Filter

Funds: 

National Natural Science Foundation of China 61573105

National Natural Science Foundation of China 61473081

Natural Science Foundation of Jiangsu Province BK20141341

More Information
    Author Bio:

    LIU Peng  Ph. D. candidate at the School of Automation, Southeast University. He received his bachelor degree from the College of Science, Henan University of Technology in 2006, and his master degree from the School of Mathematics and Information Science, Wenzhou University in 2011. His research interest covers the multi-agent systems, structural systems, and distributed estimate

    ZHANG Ya Associate professor at the School of Automation, Southeast University. Her research interest covers the multi-agent systems and distributed filtering theory

    Corresponding author: TIAN Yu-Ping Professor at the School of Automation, Southeast University. His research interest covers the multi-agent systems and optimization and control in communication networks. Corresponding author of this paper
  • Recommended by Associate Editor ZHU Bing
  • 摘要: 本文研究多个传感器测量非线性系统时的分布式无迹Kalman滤波器(Unscented Kalman filter, UKF)的设计问题.借助离散多智能体系统有限时间平均一致算法的思想, 针对无向通信和有向通信网络分别设计了两种不同的滤波算法.对于无向连通的通信拓扑, 利用节点存储的一致性算法的迭代值构造差向量, 由该差向量构成的Hankel矩阵的核来得到分布式无迹Kalman滤波器, 并通过利用误差协方差矩阵的逆来构造Lyapunov函数, 基于随机稳定性引理证明了该有限时间一致无迹Kalman滤波器的稳定性.对于有向强连通的通信拓扑, 结合比率一致和Hankal矩阵的核来设计分布式无迹Kalman滤波器, 该滤波器的稳定性与无向通信拓扑的滤波器相同.最后, 通过仿真例子来验证所提滤波器的跟踪效果.
    Recommended by Associate Editor ZHU Bing
    1)  本文责任编委 诸兵
  • 图  1  6个传感器构成的无向与有向通信图

    Fig.  1  Undirected and directed communication topologies of 6 sensors

    图  2  $6$个节点的平均跟踪偏差

    Fig.  2  The average tracking deviation of $6$ sensors

    图  3  $6$个节点均方估计误差的平均值

    Fig.  3  The mean value of $6$ sensors$'$ mean square estimation error

  • [1] Tian Y, Chen Z, Yin F L. Distributed IMM-unscented Kalman filter for speaker tracking in microphone array networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015, 23(10): 1637-1647 doi: 10.1109/TASLP.2015.2442418
    [2] Singh A K, Pal B C. Decentralized dynamic state estimation in power systems using unscented transformation. IEEE Transactions on Power Systems, 2014, 29(2): 794-804 http://cn.bing.com/academic/profile?id=a99fd7954029c6ae1fc2e92133b58d69&encoded=0&v=paper_preview&mkt=zh-cn
    [3] Qing X Y, Karimi H R, Niu Y G, et al. Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems. International Journal of Electrical Power & Energy Systems, 2015, 65: 26-33 http://cn.bing.com/academic/profile?id=236ef97621030d94b4ba7133c5284fba&encoded=0&v=paper_preview&mkt=zh-cn
    [4] Li W L, Jia Y M. Consensus-based distributed multiple model UKF for jump Markov nonlinear systems. IEEE Transactions on Automatic Control, 2012, 57(1): 230-236 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=92ab431dde19c263bdfaffc1e1579315
    [5] Battistelli G, Chisci L, Mugnai G, et al. Consensus-based linear and nonlinear filtering. IEEE Transactions on Automatic Control, 2015, 60(5): 1410-1415 doi: 10.1109/TAC.2014.2357135
    [6] Yuan Y, Shi L, Liu J, et al. Distributed Kalman filtering with minimum-time consensus algorithm[EB/OL]. arXiv: 1703.05438v1[cs.SY], March 16, 2017.
    [7] Li W L, Jia Y M. Distributed estimation for Markov jump systems via diffusion strategies. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(1): 448-460 doi: 10.1109/TAES.2017.2650801
    [8] Olfati-Saber R, Murray R. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533 doi: 10.1109/TAC.2004.834113
    [9] Gu D B, Sun J X, Hu Z, et al. Consensus based distributed particle filter in sensor networks. In: Proceedings of the 2008 IEEE International Conference on Information and Automation. Zhangjiajie, China, 2008. 302-307
    [10] Liu P, Tian Y P, Zhang Y. Distributed Kalman filtering with finite-time max-consensus protocol. IEEE Access, 2018, 6: 10795-10802 doi: 10.1109/ACCESS.2018.2809451
    [11] Battistelli G, Chisci L. Stability of consensus extended Kalman filtering for distributed state estimation. Automatica, 2016, 68: 169-178 doi: 10.1016/j.automatica.2016.01.071
    [12] Ren W, Al-Saggaf U M. Distributed Kalman-Bucy filter with embedded dynamic averaging algorithm. IEEE Systems Journal, 2018, 12(2): 1722-1730 doi: 10.1109/JSYST.2017.2657765
    [13] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3): 401-422 http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs-e200801002
    [14] Gustafsson F, Hendeby G. Some relations between extended and unscented Kalman filters. IEEE Transactions on Signal Processing, 2012, 60(2): 545-555 doi: 10.1109/TSP.2011.2172431
    [15] Chang L B, Hu B Q, Li A, et al. Transformed unscented Kalman filter. IEEE Transactions on Automatic Control, 2013, 58(1): 252-257 doi: 10.1109/TAC.2012.2204830
    [16] Menegaz H M T, Ishihara J Y, Borges G A. New minimum sigma set for unscented filtering. International Journal of Robust and Nonlinear Control, 2015, 25(7): 3286-3298 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=a3129dbc2f74d32c6b77dfcc8403baf7
    [17] Menegaz H M T, Ishihara J Y, Borges G A, et al. A systematization of the unscented Kalman filter theory. IEEE Transactions on Automatic Control, 2015, 60(10): 2583-2598 doi: 10.1109/TAC.2015.2404511
    [18] Dunik J, Simandl M, Straka O. Unscented Kalman filter: Aspects and adaptive setting of scaling parameter. IEEE Transactions on Automatic Control, 2012, 57(9): 2411-2416 doi: 10.1109/TAC.2012.2188424
    [19] Straka O, Dunik J, Simandl M. Unscented Kalman filter with advanced adaptation of scaling parameter. Automatica, 2014, 50(10): 2657-2664 doi: 10.1016/j.automatica.2014.08.030
    [20] Vercauteren T, Wang X D. Decentralized sigma-point information filters for target tracking in collaborative sensor networks. IEEE Transactions on Signal Processing, 2005, 53(8): 2997-3009 doi: 10.1109/TSP.2005.851106
    [21] Lee D J. Nonlinear estimation and multiple sensor fusion using unscented information filtering. IEEE Signal Processing Letters, 2008, 15: 861-864 doi: 10.1109/LSP.2008.2005447
    [22] Liu G L, Worgotter F, Markelic I. Square-root sigma-point information filtering. IEEE Transactions on Automatic Control, 2012, 57(11): 2945-2950 doi: 10.1109/TAC.2012.2193708
    [23] Li W Y, Wei G L, Han F, et al. Weighted average consensus-based unscented Kalman filtering. IEEE Transactions on Cybernetics, 2016, 46(2): 558-567 doi: 10.1109/TCYB.2015.2409373
    [24] Liu G L, Tian G H. Square-root sigma-point information consensus filters for distributed nonlinear estimation. Sensors, 2017, 17(4): 800 doi: 10.3390/s17040800
    [25] Reif K, Gunther S, Yaz E, et al. Stochastic stability of the discrete-time extended Kalman filter. IEEE Transactions on Automatic Control, 1999, 44(4): 714-728 doi: 10.1109/9.754809
    [26] Li L, Xia Y Q. Stochastic stability of the unscented Kalman filter with intermittent observations. Automatica, 2012, 48(5): 978-981 doi: 10.1016/j.automatica.2012.02.014
    [27] Yuan Y, Stan G B, Shi L, et al. Decentralised minimum-time consensus. Automatica, 2013, 49(5): 1227-1235 doi: 10.1016/j.automatica.2013.02.015
    [28] Charalambous T, Yuan Y, Yang T, et al. Distributed finite-time average consensus in digraphs in the presence of time-delays. IEEE Transactions on Control of Network Systems, 2015, 2(4): 370-381 doi: 10.1109/TCNS.2015.2426732
    [29] Yang T, Wu D, Sun Y N, et al. Minimum-time consensus-based approach for power system applications. IEEE Transactions on Industrial Electronics, 2016, 63(2): 1318-1328 doi: 10.1109/TIE.2015.2504050
    [30] Xiao L, Boyd S. Fast linear iterations for distributed averaging. Systems & Control Letters, 2004, 53: 65-78 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_0707.0500
    [31] Horn R, Johnson C. Matrix Analysis. New York: Cambridge University Press, 1985
    [32] Xiong K, Zhang H Y, Chan C W. Performance evaluation of UKF-based nonlinear filtering. Automatica, 2006, 42(2): 261-270 doi: 10.1016/j.automatica.2005.10.004
  • 加载中
图(3)
计量
  • 文章访问数:  296
  • HTML全文浏览量:  46
  • PDF下载量:  172
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-12-25
  • 录用日期:  2018-04-04
  • 刊出日期:  2020-07-24

目录

    /

    返回文章
    返回