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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

段广全 孙书利

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 doi: 10.16383/j.aas.c180270
引用本文: 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 doi: 10.16383/j.aas.c180270
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 doi: 10.16383/j.aas.c180270
Citation: 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 doi: 10.16383/j.aas.c180270

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

doi: 10.16383/j.aas.c180270
基金项目: 

国家自然科学基金 61573132

详细信息
    作者简介:

    段广全  黑龙江大学电子工程学院硕士研究生.主要研究方向为状态融合估计和系统辨识. E-mail: dsnx369@163.com

    通讯作者:

    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.本文通信作者. E-mail: sunsl@hlju.edu.cn

  • 本文责任编委 张俊

Self-tuning Distributed Fusion Estimation for Systems With Unknown Model Parameters and Fading Measurement Rates

Funds: 

National Natural Science Foundation of China 61573132

More Information
    Author Bio:

    DUAN Guang-Quan  Master student at the School of Electronic Engineering, Heilongjiang University. His research interest covers state fusion estimation and system identification

    Corresponding author: SUN Shu-Li  Professor at the School of Electronic Engineering, Heilongjiang University. His research interest covers state estimation and multi-sensor information fusion. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Jun
  • 摘要: 研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下, 应用递推增广最小二乘(Recursive extend least squares, RELS)算法和加权融合估计算法提出了分布式融合未知模型参数辨识器; 应用相关函数对描述衰减观测现象的随机变量的数学期望和方差进行在线辨识.将辨识后的模型参数、数学期望和方差代入到最优分布式融合状态滤波器中, 获得了相应的自校正融合状态滤波算法.应用动态误差系统分析(Dynamic error system analysis, DESA)方法证明了算法的收敛性.仿真例子验证了算法的有效性.
    Recommended by Associate Editor ZHANG Jun
    1)  本文责任编委 张俊
  • 图  1  $\Phi$中未知参数估计

    Fig.  1  Identification of parameters of $\Phi$

    图  2  $a_{11}$估计误差方差

    Fig.  2  Estimation error variance of $a_{11}$

    图  3  $a_{12}$估计误差方差

    Fig.  3  Estimation error variance of $a_{12}$

    图  4  $\mu_{i}(t)$的数学期望辨识

    Fig.  4  Identification of Mathematical expectation of $\mu_{i}(t)$

    图  5  $\mu_{i}(t)$的方差辨识

    Fig.  5  Identification of variance of $\mu_{i}(t)$

    图  6  自校正状态分量1融合滤波器

    Fig.  6  The first state component of self-tuning fusion filter

    图  7  自校正状态分量2融合滤波器

    Fig.  7  The second state component of self-tuning fusion filter

    图  8  局部、融合最优与自校正状态分量1的滤波误差方差

    Fig.  8  Variance of the first state component of local, fusion optimal and self-tuning filters

    图  9  局部、融合最优与自校正状态分量2的滤波误差方差

    Fig.  9  Variance of the second state component of local, fusion optimal and self-tuning filters

    图  10  考虑衰减观测与没有考虑衰减观测自校正融合滤波器的均方误差的迹

    Fig.  10  Trace of mean square error of the self-tuning fusion filters with/without considering fading measurements

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  • 被引次数: 0
出版历程
  • 收稿日期:  2018-05-02
  • 录用日期:  2018-11-01
  • 刊出日期:  2021-02-26

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