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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, x(x): 1−10 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, x(x): 1−10 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, x(x): 1−10 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, x(x): 1−10 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与机动目标跟踪

    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

    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. 本文通讯作者. E-mail: fuhp@sjtu.edu.cn

Consensus-based Distributed Variable Structure Multiple Model

Funds: Supported by National Natural Science Foundation of P. R. China (61627810)
  • 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. 仿真试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

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