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基于一致性的分布式变结构多模型方法

王昱淇 卢宙 蔡云泽

王昱淇,  卢宙,  蔡云泽.  基于一致性的分布式变结构多模型方法.  自动化学报,  2021,  47(7): 1548−1557 doi: 10.16383/j.aas.c190091
引用本文: 王昱淇,  卢宙,  蔡云泽.  基于一致性的分布式变结构多模型方法.  自动化学报,  2021,  47(7): 1548−1557 doi: 10.16383/j.aas.c190091
Wang Yu-Qi,  Lu Zhou,  Cai Yun-Ze.  Consensus-based distributed variable structure multiple model.  Acta Automatica Sinica,  2021,  47(7): 1548−1557 doi: 10.16383/j.aas.c190091
Citation: Wang Yu-Qi,  Lu Zhou,  Cai Yun-Ze.  Consensus-based distributed variable structure multiple model.  Acta Automatica Sinica,  2021,  47(7): 1548−1557 doi: 10.16383/j.aas.c190091

基于一致性的分布式变结构多模型方法

doi: 10.16383/j.aas.c190091
基金项目: 国家自然科学基金重大科研仪器研制项目(61627810)资助
详细信息
    作者简介:

    王昱淇:上海交通大学电子信息与电器工程学院智能信息控制实验室博士研究生. 主要研究方向为分布式多传感器网络, 机动目标跟踪.E-mail: wangyuqi@sjtu.edu.cn

    卢宙:上海交通大学电子信息与电器工程学院智能信息控制实验室硕士研究生. 主要研究方向为非线性滤波与机动目标跟踪.E-mail: luzhou_sjtu@163.com

    蔡云泽:上海交通大学电子信息与电器工程学院研究员. 主要研究方向为机动目标跟踪, 复杂网络滤波, 信息融合与计算机视觉. 本文通信作者. E-mail: fuhp@sjtu.edu.cn

Consensus-based Distributed Variable Structure Multiple Model

Funds: Supported by National Natural Science Foundation of China (61627810)
More Information
    Author Bio:

    WANG Yu-Qi Ph.D. candidate at the Intelligent Information Control Laboratory (IIC Lab), School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. His research interest covers distributed multi-sensor networks and maneuvering target tracking

    LU Zhou Master student at the Intelligent Information Control Laboratory (IIC Lab), School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. His research interest covers nonlinear system estimation and maneuvering target tracking

    CAI Yun-Ze Professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. Her research interest covers target tracking, estimation in complex networks, information fusion, and computer vision. Corresponding author of this paper

  • 摘要:

    本文针对由雷达与红外组成的分布式传感器网络, 研究基于一致性的分布式变结构多模型方法(Distributed variable structure multiple model, DVSMM). 首先,使用无迹信息滤波(Unscented information filter, UIF)解决系统非线性的问题, 然后,将变结构交互式多模型(Variable structure interacting multiple model, VSMM)方法进行改进, 提出一类可应用于分布式状态估计的分布式变结构多模型DVSMM方法. 仿真实验结果验证了该方法的有效性.

  • 图  1  用无向图表示的传感器网络

    Fig.  1  A sensor network expressed by undirected graph

    图  2  雷达和红外传感器量测模型

    Fig.  2  Measurement model of radar and infrared

    图  3  $ {\theta }_{1}-{\theta }_{2} $的定义

    Fig.  3  Definition of $ {\theta }_{1}-{\theta }_{2} $

    图  4  交互模型预测信息的DIMM方法示意图

    Fig.  4  Diagram of DIMM with mode-matched PDFs

    图  5  分布式变结构多模型方法面临难题

    Fig.  5  The difficulty in achieving DVSMM

    图  6  DVSMM更新模型集方法流程图

    Fig.  6  Diagram of DVSMM updating model set

    图  7  模式空间内的13个基础模型

    Fig.  7  Basic model-set with 13 models

    图  8  目标运动轨迹与传感器类型

    Fig.  8  Target positions and sensors types

    图  9  平均位置估计误差

    Fig.  9  Average position estimation error

    图  12  平均速度估计一致性误差

    Fig.  12  Average velocity estimation consensus error

    图  10  平均速度估计误差

    Fig.  10  Average velocity estimation error

    图  11  平均位置估计一致性误差

    Fig.  11  Average position estimation consensus error

    表  1  目标运动模式的变化

    Table  1  Target mode switching

    时间k1 ~ 5050 ~ 100100 ~ 150150 ~ 200200 ~ 250250 ~ 300
    加速度${u_k}$$ {\left[\rm{0}, \rm{0}\right]}^{\rm{T}}$$ {\left[\rm{0}, \rm{-20}\right]}^{\rm{T}}$$ {\left[\rm{0}, \rm{0}\right]}^{\rm{T}}$$ {\left[\rm{10}, \rm{10}\right]}^{\rm{T}}$$ {\left[\rm{-10}, \rm{-10}\right]}^{\rm{T}}$$ {\left[\rm{10}, \rm{10}\right]}^{\rm{T}}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-02-18
  • 录用日期:  2019-12-02
  • 网络出版日期:  2021-02-27
  • 刊出日期:  2021-07-27

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