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

带时间相关乘性噪声多传感器系统的分布式融合估计

马静 杨晓梅 孙书利

马静, 杨晓梅, 孙书利. 带时间相关乘性噪声多传感器系统的分布式融合估计. 自动化学报, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210147
引用本文: 马静, 杨晓梅, 孙书利. 带时间相关乘性噪声多传感器系统的分布式融合估计. 自动化学报, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210147
Ma Jing, Yang Xiao-Mei, Sun Shu-Li. Distributed fusion estimation for multi-sensor systems with time-correlated multiplicative noises. Acta Automatica Sinica, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210147
Citation: Ma Jing, Yang Xiao-Mei, Sun Shu-Li. Distributed fusion estimation for multi-sensor systems with time-correlated multiplicative noises. Acta Automatica Sinica, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210147

带时间相关乘性噪声多传感器系统的分布式融合估计

doi: 10.16383/j.aas.c210147
基金项目: 国家自然科学基金(61573132, 61873087), 黑龙江省信息融合估计与检测重点实验室资助
详细信息
    作者简介:

    马静:黑龙江大学数学科学学院教授. 主要研究方向为信息融合状态估计. E-mail: majing@hlju.edu.cn

    杨晓梅:黑龙江大学数学科学学院研究生. 主要研究方向为信息融合状态估计. E-mail: 15615088651@163.com

    孙书利:黑龙江大学电子工程学院教授. 主要研究方向为状态估计, 多传感器信息融合, 网络化系统滤波. 本文通信作者. E-mail: sunsl@hlju.edu.cn

Distributed Fusion Estimation for Multi-Sensor Systems with Time-Correlated Multiplicative Noises

Funds: Supported by National Nature Science Foundation of China (61573132, 61873087), Information Fusion Estimation and Detection Key Laboratory in Heilongjiang Province
More Information
    Author Bio:

    MA Jing Professor at the School of Mathematics Science, Heilongjia- ng University. Her research interest is information fusion state estimation

    YANG Xiao-Mei Master student at the School of Mathematics Science, Heilongjiang University. Her research interest is information fusion state estimation

    SUN Shu-Li Professor at the School of Electronic Engineering, Heilongjiang University. His research interest covers state estimation, multi-sensor information fusion and networked systems filtering. Corresponding author of this paper

  • 摘要: 本文研究了带时间相关乘性噪声多传感器系统的分布式融合估计问题. 其中时间相关的乘性噪声满足一阶Gauss-Markov过程. 通过引入虚拟状态和虚拟过程噪声, 构建了虚拟状态的递推方程. 基于新息分析方法, 分别对系统状态和虚拟状态设计了局部一步预报器. 然后基于一步预报器设计了状态的局部线性滤波器、多步预报器和平滑器. 推导了任意两个局部状态估计误差之间的互协方差矩阵. 进而, 基于线性最小方差意义下的矩阵加权、对角矩阵加权和标量加权融合算法, 给出了相应的分布式融合状态估值器. 最后, 分析了算法的稳定性. 仿真研究验证了所提算法的有效性.
  • 图  1  分布式矩阵加权融合滤波的位置跟踪性能

    Fig.  1  The position tracking performance of distributed fusion filter weighted by matrices

    图  2  RMSEs比较图

    Fig.  2  Comparisons of RMSEs

    图  3  局部滤波、标量加权融合、对角矩阵加权融合、矩阵加权融合和集中式融合滤波的误差方差

    Fig.  3  Variances of LFs, SF, DF, MF and CF

    图  4  矩阵加权融合预报器、滤波器和平滑器的估计误差方差

    Fig.  4  Variances of fusion predictor, filter and smoother weighted by matrices

  • [1] 邓自立, 王欣, 高媛. 建模与估计(第二版). 北京: 科学出版社, 2016.

    Deng Zi-Li, Wang Xin, Gao Yuan. Modeling and Estimation (Second Eedition). Beijing: The science Press, 2016.
    [2] 徐宁寿. 随机信号估计与系统控制. 北京: 北京工业大学出版社, 2001.

    Xu Ning-Shou. Random Signal Estimation and System Control. Beijing: Beijing University of Technology Press, 2001.
    [3] 祁波, 孙书利. 带未知通信干扰和丢包补偿的多传感器网络化不确定系统的分布式融合滤波. 自动化学报, 2018, 44(6): 1107--1114.

    Qi Bo, Sun Shu-Li. Distributed fusion filtering for multi-sensor networked uncertain systems with unknown communication disturbances and compensations of packet dropouts. Acta Automatica Sinica, 2018, 44(6): 1107--1114.
    [4] 李娜, 马静, 孙书利. 带多丢包和滞后随机不确定系统的最优线性估计. 自动化学报, 2015, 41(3): 611--619.

    Li Na, Ma Jing, Sun Shu-Li. Optimal linear estimation for stochastic uncertain systems with multiple packet dropouts and delays. Acta Automatica Sinica, 2015, 41(3): 611--619.
    [5] Kim K J, Iltis R A. Joint detection and channel estimation algorithms for QS-CDMA signals over time-varying channels. IEEE Transaction on Communication, 2002, 50(5): 845--855. doi: 10.1109/TCOMM.2002.1006565
    [6] Iltis R A. Joint estimation of PN code delay and multipath using the extended Kalman filter. IEEE Transactions on Automatic Control, 1990, 38(10): 1677--1685.
    [7] Chow B S, Birkemeier W P. A new structure of recursive estimator. IEEE Transaction on Automatic Control, 1989, 34(5): 568--572.
    [8] Chow B S, Birkemeier W P. A new recursive filter for systems with multiplicative noise. IEEE Transactions on Information Theory, 1990, 36(6): 1430--1435. doi: 10.1109/18.59939
    [9] Zhang L, Zhang X. An optimal filtering algorithm for systems with multiplicative/additive noises. IEEE Signal Processing Letters, 2007, 14(7): 469--472. doi: 10.1109/LSP.2006.891331
    [10] Yu X, Jin G, Li J. Target tracking algorithm for system with Gaussian/non-Gaussian multiplicative noise. IEEE Transactions on Vehicular Technology, 2020, 69(1): 90--100. doi: 10.1109/TVT.2019.2952368
    [11] Liu W. Optimal filtering for discrete-time linear systems with time-correlated multiplicative measurement noises. IEEE Transactions on Automatic Control, 2016, 61(7): 1972--1978. doi: 10.1109/TAC.2015.2480238
    [12] Wang S, Wang Z, Dong H, F E Alsaadi. Recursive state estimation for linear systems with lossy measurements under time-correlated multiplicative noises. Journal of the Franklin Institute, 2020, 57: 1887--1908.
    [13] Liu W, Shi P. Optimal linear filtering for networked control systems with time-correlated fading channels, Automatica, 2019, 101: 345--353. doi: 10.1016/j.automatica.2018.11.042
    [14] Li W, Jia Y, Du J. Tobit Kalman filter with time-correlated multiplicative measurement noise. IET Control Theory and Applications, 2017, 11(1): 122--128. doi: 10.1049/iet-cta.2016.0624
    [15] Geng H, Wang Z, Liang Y, Cheng Y, Alsaadi F E. Tobit Kalman filter with time-correlated multiplicative sensor noises under redundant channel transmission. IEEE Sensors Journal, 2017, 17(24): 8367--8377. doi: 10.1109/JSEN.2017.2766077
    [16] Lin H, Sun S. Globally optimal sequential and distributed fusion state estimation for multi-sensor systems with cross-correlated noises. Automatica, 2019, 101: 128--137. doi: 10.1016/j.automatica.2018.11.043
    [17] Carlson N A. Federated square root filter for decentralized parallel processes. IEEE Transactons on Aerospace and Electronic Systems, 1990, 26(3): 517--525. doi: 10.1109/7.106130
    [18] Kim K H. Development of track to track fusion algorithm. In: Proceedings of American Control Conference. Maryland, USA: IEEE, 1994.1037−1041.
    [19] Sun S, Deng Z. Multi-sensor optimal information fusion Kalman filter. Automatica, 2004, 40(6): 1017--1023. doi: 10.1016/j.automatica.2004.01.014
    [20] 从金亮, 李银伢, 戚国庆, 盛安冬. 快速协方差交叉融合算法及应用. 自动化学报, 2020, 46(7): 1433--1444.

    Cong Jin-Liang, Li Yin-Ya, Qi Guo-Qing, Sheng An-Dong. A fast covariance intersection fusion algorithm and its application. Acta Automatica Sinica, 2020, 46(7): 1433--1444.
    [21] 王雪梅, 刘文强, 邓自立. 不确定系统改进的鲁棒协方差交叉融合稳态Kalman预报器, 自动化学报, 2016, 42(8): 1198--1206.

    Wang Xue-Mei, Liu Wen-Qiang, Deng Zi-Li. Modified robust covariance intersection fusion steady-state Kalman predictor for uncertain systems. Acta Automatica Sinica, 2016, 42(8): 1198--1206.
    [22] Sun S. Distributed optimal linear fusion estimators. Information fusion, 2020, 63: 56--73. doi: 10.1016/j.inffus.2020.05.006
    [23] Sun S. Distributed optimal linear fusion predictors and filters for systems with random parameter matrices and correlated noise. IEEE Transactons on Signal Processing, 2020, 68: 1064--1074. doi: 10.1109/TSP.2020.2967180
    [24] Ma J, Sun S. Distributed fusion filter for networked stochastic uncertain systems with transmission delays and packet dropouts. Signal Processing, 2017, 130: 268--278. doi: 10.1016/j.sigpro.2016.07.004
    [25] Tian T, Sun S, Lin H. Distributed fusion filter for multi-sensor systems with finite-step correlated noises. Information Fusion, 2019, 46: 128--140. doi: 10.1016/j.inffus.2018.05.002
    [26] Wang X, Liu W, Deng Z. Robust weighted fusion Kalman estimators for multi-model multisensor systems with uncertain-variance multiplicative and linearly correlated additive white noises. Signal Processing, 2017, 137: 339--355. doi: 10.1016/j.sigpro.2017.02.015
    [27] Yang X, Song Z, Ma J. Distributed fusion filter for systems with time-correlated multiplicative noise. In: Proceedings of the 39th Chinese Control Conference, Shenyang, China: IEEE, 2020.2741−2745.
    [28] 孙书利, 马静. 随机奇异系统的分布式降阶最优融合Kalman滤波器. 自动化学报, 2006, 32(20): 286--290.

    Sun Shu-Li, Ma Jing, Distributed reduced-order optimal fusion Kalman filters for stochastic singular system. Acta Automatica Sinica, 2006, 32(20): 286--290.
    [29] Sun Shu-Li, Wang Guang-Hui, Modeling and estimation for networked systems with multiple random transmission delays and packet losses. Systems & Control Letters, 2014, 73, 6--16.
    [30] 段广全, 孙书利. 带未知模型参数和衰减观测率系统自校正分布式融合估计. 自动化学报, 2021, 47(2): 423--431.

    DUAN Guang-Quan, SUN Shu-Li. Self-tuning distributed fusion estimation for systems with unknown model parameters and fading measurement rates. Acta Automatica Sinica, 2021, 47(2): 423--431.
  • 加载中
计量
  • 文章访问数:  102
  • HTML全文浏览量:  47
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-02-18
  • 修回日期:  2021-06-25
  • 网络出版日期:  2021-08-16

目录

    /

    返回文章
    返回