2.765

2022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

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
  • [1] Blom H A P. An efficient filter for abruptly changing systems. In: Proceedings of the 23rd IEEE Conference on Decision and Control, Las Vegas, USA, 1984. 656−658
    [2] Li X R. Model-set sequence-conditioned estimation for variable-structure MM estimation. In: Proceedings of Signal and Data Processing of Small Targets, Orlando, United States, 1998. 546−558
    [3] Li X R. Multiple-model estimation with variable structure. II. Model-set adaptation. IEEE Transactions on Automatic Control, 2000, 45(11): 2047−2060 doi: 10.1109/9.887626
    [4] Li X R, Zhang Y M. Multiple-model estimation with variable structure. V. Likely-model set algorithm. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 448−466 doi: 10.1109/7.845222
    [5] Li X R, Jilkov V P, Ru J. Multiple-model estimation with variable structure - Part VI: Expected-mode augmentation. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 853−867 doi: 10.1109/TAES.2005.1541435
    [6] Xiong N, Svensson P. Multi-sensor management for information fusion: issues and approaches. Information fusion, 2002, 3(2): 163−186 doi: 10.1016/S1566-2535(02)00055-6
    [7] Carli R, Chiuso A, Schenato L. Distributed Kalman filtering based on consensus strategies. IEEE Journal on Selected Areas In Communications, 2008, 26(4): 622−633 doi: 10.1109/JSAC.2008.080505
    [8] Olfati-Saber R. Distributed Kalman filtering for sensor networks. In: Proceedings of 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007. 5492−5498
    [9] Olfati-Saber R. Distributed Kalman filter with embedded consensus filters. In: Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, 2005. 8179−8184
    [10] Casbeer D W, Beard R. Distributed information filtering using consensus filters. In: Proceedings of American Control Conference, Saint Louis, USA, 2009. 1882−1887
    [11] Casbeer D W, Beard R. Multi-static radar target tracking using information consensus filters. In: Proceedings of AIAA Guidance, Navigation, and Control Conference, Chicago, USA, 2009. 6223−6232
    [12] Stanković S S, Stanković M S, Stipanović D M. Consensus based overlapping decentralized estimation with missing observations and communication faults. Automatica, 2009, 45(6): 1397−1406 doi: 10.1016/j.automatica.2009.02.014
    [13] Lin P, Jia Y, Li L. Distributed robust H consensus control in directed networks of agents with time-delay. Systems & Control Letters, 2008, 57(8): 643−653
    [14] Spanos D P, Olfati-Saber R, Murray R M. Dynamic consensus on mobile networks. In: Proceedings of International Federation of Automatic Control, Prague, Czech Republic, 2005. 1−6
    [15] Xi Feng, Liu Zhong. Distributed Kalman filter with information matrix weighted consensus strategies. Information and Control, 2010, 39(2): 194−199
    [16] Shi L, Johansson K H, Murray R M. Change sensor topology when needed: How to efficiently use system resources in control and estimation over wireless networks. In: Proceedings of 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007. 5478−5485
    [17] Yu W, Chen G, Wang Z. Distributed consensus filtering in sensor networks. IEEE Transactions on Systems, Man, and Cyberneticsb - Part B: Cybernetics, 2009, 39(6): 1568−1577 doi: 10.1109/TSMCB.2009.2021254
    [18] Li W, Jia Y. Consensus-based distributed multiple model UKF for jump Markov nonlinear systems. IEEE Transactions on Automatic Control, 2011, 57(1): 227−233
    [19] Ding Z, Liu Y, Liu J. Adaptive interacting multiple model algorithm based on information-weighted consensus for maneuvering target tracking. Sensors, 2018, 18(7): 2012−2035 doi: 10.3390/s18072012
    [20] Fantacci C, Battistelli G, Chisci L. Multiple-model algorithms for distributed tracking of a maneuvering target. In: Proceedings of 15th International Conference on Information Fusion, Singapore, Singapore, 2012. 1028−1035
    [21] Lee D J. Nonlinear estimation and multiple sensor fusion using unscented information filtering. IEEE Signal Processing Letters, 2008, 15: 861−864 doi: 10.1109/LSP.2008.2005447
    [22] Julier S J, Uhlmann J K, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000, 45(3): 477−482 doi: 10.1109/9.847726
    [23] Wan E A, Van D M R. The unscented Kalman filter for nonlinear estimation. In: Proceedings of Adaptive Systems for Signal Processing, Communications, and Control Symposium, Lake Louise, Canada, 2000. 153−158
    [24] Li X R, Jilkov V P. Survey of maneuvering target tracking. Part V. Multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255−1321 doi: 10.1109/TAES.2005.1561886
    [25] Xiao L, Boyd S, Lall S. A scheme for robust distributed sensor fusion based on average consensus. In: Proceedings of International Symposium on Information Processing in Sensor Networks, Boise, USA, 2005. 63−70
  • 加载中
图(12) / 表(1)
计量
  • 文章访问数:  926
  • HTML全文浏览量:  381
  • PDF下载量:  196
  • 被引次数: 0
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

目录

    /

    返回文章
    返回