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多率量测下随机跳变系统迁移交互多模型估计

高爽 栾小丽 赵顺毅 刘飞

高爽, 栾小丽, 赵顺毅, 刘飞. 多率量测下随机跳变系统迁移交互多模型估计. 自动化学报, 2022, 48(x): 1−9 doi: 10.16383/j.aas.c220011
引用本文: 高爽, 栾小丽, 赵顺毅, 刘飞. 多率量测下随机跳变系统迁移交互多模型估计. 自动化学报, 2022, 48(x): 1−9 doi: 10.16383/j.aas.c220011
Gao Shuang, Luan Xiao-Li, Zhao Shun-Yi, Liu Fei. Transfer interacting multiple model state estimator for Markovian jump linear systems with multi-rate measurements. Acta Automatica Sinica, 2022, 48(x): 1−9 doi: 10.16383/j.aas.c220011
Citation: Gao Shuang, Luan Xiao-Li, Zhao Shun-Yi, Liu Fei. Transfer interacting multiple model state estimator for Markovian jump linear systems with multi-rate measurements. Acta Automatica Sinica, 2022, 48(x): 1−9 doi: 10.16383/j.aas.c220011

多率量测下随机跳变系统迁移交互多模型估计

doi: 10.16383/j.aas.c220011
基金项目: 国家自然科学基金(61991402, 61833007, 61991400), 江苏省研究生科研与实践创新计划(KYCX21-2007) 资助
详细信息
    作者简介:

    高爽:江南大学物联网工程学院博士研究生. 2018年获得江南大学学士学位. 主要研究方向为状态估计, 贝叶斯估计理论. E-mail: gaoshuang@stu.jiangnan.edu.cn

    栾小丽:江南大学物联网工程学院教授. 2010年获得江南大学博士学位. 主要研究方向为复杂工业过程建模、控制与优化. 本文通信作者. E-mail: xlluan@jiangnan.edu.cn

    赵顺毅:江南大学物联网工程学院研究员. 2015年获得江南大学博士学位. 主要研究方向为随机信号处理, 贝叶斯估计理论和故障检测与诊断. E-mail: shunyi.s.y@gmail.com

    刘飞:江南大学物联网工程学院教授. 2002年获得浙江大学博士学位. 主要研究方向为过程控制, 过程系统工程. E-mail: fliu@jiangnan.edu.cn

Transfer Interacting Multiple Model State Estimator for Markovian Jump Linear Systems With Multi-rate Measurements

Funds: Supported by National Natural Science Foundation of China (61991402, 61833007, 61991400) and the Postgraduate Research Practice Innovation Program of Jiangsu Province (KYCX21-2007)
More Information
    Author Bio:

    GAO Shuang Ph.D. candidate at the School of Internet of Things Engineering, Jiangnan University. She received her bachelor degree from Jiangnan University in 2018. Her research interest covers state estimation and Bayesian estimation theory

    LUAN Xiao-Li Professor at the School of Internet of Things Engineering, Jiangnan University. She received her Ph.D. degree in control theory and control engineering from Jiangnan University in 2010. Her main research interest is modeling, control and optimization of complex industrial process. Corresponding author of this paper

    ZHAO Shun-Yi Researcher at the School of Internet of Things Engineering, Jiangnan University. He received his Ph.D. degree in control theory and application from Jiangnan University in 2015. His research interest covers statistical signal processing, Bayesian estimation theory, and fault detection and diagnosis

    LIU Fei Professor at the School of Internet of Things Engineering, Jiangnan University. He received his Ph.D. degree in control science and control engineering from Zhejiang University in 2002. His research interest covers process control and process system engineering

  • 摘要: 实际工业过程中, 量测数据除了在线仪表采集的快速率数据, 还有离线化验等慢速率辅助量测数据. 为了更好地利用离线化验数据, 增加在线估计的精度, 本文针对随机跳变系统, 引入迁移学习思想, 提出迁移交互多模型估计新策略. 首先, 将离线化验数据的边缘分布作为可以迁移的知识, 迁移到贝叶斯后验分布, 实现辅助量测数据的充分利用. 其次, 利用KL (Kullback-Leibler) 散度度量知识迁移前后任务间的差异性, 求解最优的贝叶斯迁移估计器. 同时, 结合慢速率量测, 利用平滑策略获取待迁移的估计值, 解决多率量测下的迁移估计难题. 然后, 利用影响力函数构建辅助量测数据与估计性能之间的解析关系, 从而对迁移效果进行定量评价. 最后, 通过在目标跟踪实例中的应用, 表明本文所提方法的有效性及优越性.
  • 图  1  多率量测过程. 实线表示真实状态, 虚线表示目标域量测 (在线快速率数据), 点表示源域量测 (离线化验数据), 其采样时间可能不规律

    Fig.  1  Multiple source measurements of the process with different sampling rates. Solid lines are true states, and dashed lines represent target measurements. Dots denote source measurements, whose sample time may be irregular

    图  2  迁移交互多模型估计器结构图

    Fig.  2  Basic operation diagram of the transfer interacting multiple model state estimator

    图  3  源域数据对迁移估计器的影响力曲线

    Fig.  3  The influence function of source measurements on the transfer state estimator

    图  4  不同算法在运动目标跟踪中的均方根误差

    Fig.  4  RMSEs of different algorithms for the moving target tracking

    图  5  源域数据质量对迁移估计器算法性能的影响

    Fig.  5  Demonstration of the performance of IMM-TF in the presence of source measurements of different quality

    图  6  基于影响力值的迁移估计器算法性能

    Fig.  6  Demonstration of the performance of IF-TF

    表  1  不同数量的源域数据迁移后的 RMSEs

    Table  1  Average RMSEs (per sample) of IMM-IF in the presence of different amount source measurements

    源域数据比重 (%)位置 (m)速度 (m/s)
    105.87552.0266
    305.40701.9567
    504.88681.8798
    704.53141.8465
    994.09991.7674
    下载: 导出CSV
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  • 收稿日期:  2022-01-04
  • 录用日期:  2022-07-21
  • 网络出版日期:  2022-11-02

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