2.765

2022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

带一步随机延迟量测非线性序列贝叶斯估计的条件后验克拉美罗下界

张勇刚 黄玉龙 李宁 赵琳

张勇刚, 黄玉龙, 李宁, 赵琳. 带一步随机延迟量测非线性序列贝叶斯估计的条件后验克拉美罗下界. 自动化学报, 2015, 41(3): 559-574. doi: 10.16383/j.aas.2015.c140391
引用本文: 张勇刚, 黄玉龙, 李宁, 赵琳. 带一步随机延迟量测非线性序列贝叶斯估计的条件后验克拉美罗下界. 自动化学报, 2015, 41(3): 559-574. doi: 10.16383/j.aas.2015.c140391
ZHANG Yong-Gang, HUANG Yu-Long, LI Ning, ZHAO Lin. Conditional Posterior Cramér-Rao Lower Bound for Nonlinear Sequential Bayesian Estimation with One-step Randomly Delayed Measurements. ACTA AUTOMATICA SINICA, 2015, 41(3): 559-574. doi: 10.16383/j.aas.2015.c140391
Citation: ZHANG Yong-Gang, HUANG Yu-Long, LI Ning, ZHAO Lin. Conditional Posterior Cramér-Rao Lower Bound for Nonlinear Sequential Bayesian Estimation with One-step Randomly Delayed Measurements. ACTA AUTOMATICA SINICA, 2015, 41(3): 559-574. doi: 10.16383/j.aas.2015.c140391

带一步随机延迟量测非线性序列贝叶斯估计的条件后验克拉美罗下界

doi: 10.16383/j.aas.2015.c140391
基金项目: 

国家自然科学基金(61001154, 61201409, 61371173), 中国博士后科学基金(2013M530147, 2014T70309), 黑龙江省博士后基金(LBH-Z13052, LBH-TZ0505),哈尔滨工程大学中央高校基本科研业务费专项基金(HEUCFX41307) 资助

详细信息
    作者简介:

    张勇刚 哈尔滨工程大学自动化学院研究员.2007年获得英国Cardiff大学博士学位.主要研究方向为光纤陀螺, 惯性导航, 滤波算法, 组合导航. E-mail: zhangyg@hrbeu.edu.cn

    通讯作者:

    黄玉龙 哈尔滨工程大学自动化学院博士研究生.主要研究方向为惯性导航, 滤波算法, 组合导航.本文通信作者. E-mail: heuedu@163.com

Conditional Posterior Cramér-Rao Lower Bound for Nonlinear Sequential Bayesian Estimation with One-step Randomly Delayed Measurements

Funds: 

Supported by National Natural Science Foundation of China (61001154, 61201409, 61371173), China Postdoctoral Science Foundation (2013M530147, 2014T70309), Heilongjiang Postdoctoral Fund (LBH-Z13052, LBH-TZ0505), and Fundamental Research Funds for the Central Universities of Harbin Engineering University (HEUCFX41307)

  • 摘要: 为了解决带一步随机延迟量测非线性状态估计器可获得最优性能的评价问题,提出了一种适用于带一步随机延迟量测非线性系统的条件后验克拉美罗下界(Conditional posterior Cramr-Rao lower bound, CPCRLB),且现有的CPCRLB仅是所提出的CPCRLB在延迟概率为零时的一种特例. 为了递归地计算提出的CPCRLB,本文提出了一种带一步随机延迟量测的粒子滤波器(Particle filter, PF),继而推导了提出的CPCRLB 一般近似解和在高斯噪声情况下的特殊近似解. 单变量非平稳增长模型、纯方位跟踪和频率调制信号模型的数值仿真证明了本文提出方法与现有方法相比的有效性和优越性.
  • [1] Arasaratnam I, Haykin S. Cubature Kalman filter. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269
    [2] [2] Jia B, Xin M, Cheng Y. High-degree cubature Kalman filter. Automatica, 2013, 49(2): 510-518
    [3] Zhang Yong-Gang, Huang Yu-Long, Wu Zhe-Min, Li Ning. A high order unscented Kalman filtering method. Acta Automatica Sinica, 2014, 40(5): 838-848(张勇刚, 黄玉龙, 武哲民, 李宁. 一种高阶无迹卡尔曼滤波方法. 自动化学报, 2014, 40(5): 838-848)
    [4] [4] Jia B, Xin M, Cheng Y. Sparse-grid quadrature nonlinear filtering. Automatica, 2012, 48(2): 327-341
    [5] [5] Dunk J, Straka O, imandl M. Stochastic integration filter. IEEE Transactions on Automatic Control, 2013, 58(6): 1561-1566
    [6] [6] Zhang X C. A novel cubature Kalman filter for nonlinear state estimation. In: Proceedings of the 52nd IEEE Conference on Decision and Control. Florence, Italy: IEEE, 2013. 7797-7802
    [7] [7] Wang S Y, Feng J C, Tse C K. Spherical simplex-radial cubature Kalman filter. IEEE Signal Processing Letters, 2014, 21(1): 43-46
    [8] Wang Lu, Li Guang-Chun, Qiao Xiang-Wei, Wang Zhao-Long, Ma Tao. An adaptive UKF algorithm based on maximum likelihood principle and expectation maximization algorithm. Acta Automatica Sinica, 2012, 38(7): 1200-1210(王璐, 李光春, 乔相伟, 王兆龙, 马涛. 基于极大似然准则和最大期望算法的自适应UKF算法. 自动化学报, 2012, 38(7): 1200-1210)
    [9] [9] Chang L B, Hu B Q, Li A, Qin F J. Transformed unscented Kalman Filter. IEEE Transactions on Automatic Control, 2013, 58(1): 252-257
    [10] Schn T B, Wills A, Ninness B. System identification of nonlinear state-space models. Automatica, 2011, 47(1): 39-49
    [11] Tichavsky P, Muravchik C H, Nehorai A. Posterior Cram'er-Rao bounds for discrete-time nonlinear filtering. IEEE Transactions on Signal Processing, 1998, 46(5): 1386-1396
    [12] Zuo L, Niu R X, Varshney P K. Conditional posterior Cramr-Rao lower bounds for nonlinear sequential Bayesian estimation. IEEE Transactions on Signal Processing, 2011, 59(1): 1-14
    [13] Zheng Y J, Ozdemir O, Niu R X, Varshney P K. New conditional posterior Cramr-Rao lower bounds for nonlinear sequential Bayesian estimation. IEEE Transactions on Signal Processing, 2012, 60(10): 5549-5556
    [14] Zuo L. Conditional Posterior Cramr-Rao Lower Bound and Distributed Target Tracking in Sensor Networks [Ph.D. dissertation], Syracuse University, Syracuse, USA, 2011.
    [15] Yang F W, Wang Z D, Feng G, Liu X H. Robust filtering with randomly varying sensor delay: the finite-horizon case. IEEE Transactions on Circuits and Systems I, 2009, 56(3): 664-672
    [16] Chen S J, Li Y Y, Qi G Q, Sheng A D. Adaptive Kalman estimation in target tracking mixed with random one-step delays, stochastic-bias measurements, and missing measurements. Discrete Dynamics in Nature and Society, 2013, 2013: Article ID 716915
    [17] Hermoso-Carazo A, Linares-Prez J. Extended and unscented filtering algorithms using one-step randomly delayed observations. Applied Mathematics and Computation, 2007, 190(2): 1375-1393
    [18] Hermoso-Carazo A, Linares-Prez J. Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems. Applied Mathematical Modelling, 2009, 33(9): 3705-3717
    [19] Wang X X, Liang Y, Pan Q, Zhao C H. Gaussian filter for nonlinear systems with one-step randomly delayed measurements. Automatica, 2013, 49(4): 976-986
    [20] Wang X X, Pan Q, Liang Y, Yang F. Gaussian smoothers for nonlinear systems with one-step randomly delayed measurements. IEEE Transactions on Automatic Control, 2013, 58(7): 1828-1835
    [21] Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 2004, 49(9): 1453-1461
    [22] Zhou T. Robust recursive state estimation with random measurements droppings. available at arXiv: 1401.4020v1 [cs.SY], 2014.
    [23] Ray A. Output feedback control under randomly varying distributed delays. Journal of Guidance, Control, and Dynamics, 1994, 17(4): 701-711
    [24] Horn R, Johnson C R. Matrix Analysis. New York: Cambridge University Press, 1985.
    [25] Ito K, Xiong K Q. Gaussian filters for nonlinear filtering problems. IEEE Transactions on Automatic Control, 2000, 45(5): 910-927
    [26] Bucy R S, Senne K D. Digital synthesis of non-linear filters. Automatica, 1971, 7(3): 287-298
    [27] 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
    [28] Li W L, Jia Y M. H filtering for a class of nonlinear discrete-time systems based on unscented transform. Signal Processing, 2010, 90(12): 3301-3307
    [29] Jia B, Xin M. Sparse-grid quadrature H filter for discrete-time systems with uncertain noise statistics. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(3): 1626-1636
  • 加载中
计量
  • 文章访问数:  2132
  • HTML全文浏览量:  126
  • PDF下载量:  1123
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-05-29
  • 修回日期:  2014-09-27
  • 刊出日期:  2015-03-20

目录

    /

    返回文章
    返回