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信息融合理论研究进展:基于变分贝叶斯的联合优化

潘泉 胡玉梅 兰华 孙帅 王增福 杨峰

潘泉, 胡玉梅, 兰华, 孙帅, 王增福, 杨峰. 信息融合理论研究进展:基于变分贝叶斯的联合优化. 自动化学报, 2019, 45(7): 1207-1223. doi: 10.16383/j.aas.c180029
引用本文: 潘泉, 胡玉梅, 兰华, 孙帅, 王增福, 杨峰. 信息融合理论研究进展:基于变分贝叶斯的联合优化. 自动化学报, 2019, 45(7): 1207-1223. doi: 10.16383/j.aas.c180029
PAN Quan, HU Yu-Mei, LAN Hua, SUN Shuai, WANG Zeng-Fu, YANG Feng. Information Fusion Progress: Joint Optimization Based on Variational Bayesian Theory. ACTA AUTOMATICA SINICA, 2019, 45(7): 1207-1223. doi: 10.16383/j.aas.c180029
Citation: PAN Quan, HU Yu-Mei, LAN Hua, SUN Shuai, WANG Zeng-Fu, YANG Feng. Information Fusion Progress: Joint Optimization Based on Variational Bayesian Theory. ACTA AUTOMATICA SINICA, 2019, 45(7): 1207-1223. doi: 10.16383/j.aas.c180029

信息融合理论研究进展:基于变分贝叶斯的联合优化

doi: 10.16383/j.aas.c180029
基金项目: 

国家自然科学基金 61501305

中国科协优秀中外青年交流计划 2017CASTQNJL046

国家自然科学基金 61501378

国家自然科学基金 61374159

航空基金 20165153034

西北工业大学博士论文创新项目 CX201915

国家自然科学基金 61790552

详细信息
    作者简介:

    潘泉   西北工业大学自动化学院教授, 信息融合技术教育部重点实验室主任.主要研究方向为信息融合理论及应用, 目标跟踪与识别技术, 光谱成像及图像处理.E-mail:quanpan@nwpu.edu.cn

    兰华  西北工业大学自动化学院讲师.主要研究方向为目标跟踪, 信息融合, 变分推理.E-mail:lanhua@nwpu.edu.cn

    孙帅  澳大利亚墨尔本皇家理工大学博士研究生.2017年获得西北工业大学自动化学院工学硕士学位.主要研究方向为多目标跟踪, 变分贝叶斯推理.E-mail:shuai.sun@student.rmit.edu.au

    王增福  西北工业大学自动化学院副教授.主要研究方向为信息融合理论与目标跟踪, 传感器管理, 组合优化.E-mail:zengfuwang@gmail.com

    杨峰  西北工业大学自动化学院副教授.主要研究方向为多源信息融合, 目标跟踪, 雷达数据处理.E-mail:yangfeng@nwpu.edu.cn

    通讯作者:

    胡玉梅  西北工业大学自动化学院博士研究生.现于澳大利亚墨尔本大学交流学习.主要研究方向为信息融合理论及应用, 目标跟踪与联合优化.本文通信作者. E-mail:hym_henu@163.com

Information Fusion Progress: Joint Optimization Based on Variational Bayesian Theory

Funds: 

National Natural Science Foundation of China 61501305

Excellent Chinese and Foreign Youth Exchange Programme of China Science and Technology Association 2017CASTQNJL046

National Natural Science Foundation of China 61501378

National Natural Science Foundation of China 61374159

Aviation Science Foundation 20165153034

Innovation Fundation for Doctor Dissertation of Northwestern Polytechnical CX201915

National Natural Science Foundation of China 61790552

More Information
    Author Bio:

     Professor at the School of Automation, Northwestern Polytechnical University. He is also the director of the Key Laboratory of Information Fusion Technology, Ministry of Education. His research interest covers information fusion theory and application, target tracking and recognition technology, spectral imaging and image processing

     Lecturer at the School of Automation, Northwestern Polytechnical University. His research interest covers target tracking, information fusion, and Bayesian inference

     Ph. D. candidate at RMIT University, Australia. He received his master degrees from the School of Automation, Northwestern Polytechnical University in 2017. His research interest covers multi-target tracking and variational Bayesian inference

     Associate professor at the School of Automation, Northwestern Polytechnical University. His research interest covers information fusion and target tracking, sensor management, and combinatorial optimization

     Associate professor at the School of Automation, Northwestern Polytechnical University. His research interest covers information fusion, target tracking, and radar data processing

    Corresponding author: HU Yu-Mei  Ph. D. candidate at the School of Automation, Northwestern Polytechnical University. She is currently also a visiting student in the University of Melbourne. Her research interest covers information fusion theory and application, target tracking and joint optimization. Corresponding author of this paper
  • 摘要: 通过梳理近年信息融合理论的发展,分析了复杂目标跟踪系统中存在的非线性、多模式、深耦合、网络化、高维数和未知扰动输入等问题,指出现阶段目标跟踪系统中联合优化的必要性.继而,讨论了解决联合优化问题的主要方法,包括联合检测与估计,联合聚类与估计,联合关联与估计及联合决策与估计等.同时,着重介绍了变分贝叶斯辨识、估计和优化的统一框架和以其为基础的目标跟踪联合一体优化方法,并以天波超视距雷达为应用背景,给出在多路径多模式多目标跟踪场景下算法的一般性描述.最后,讨论了变分贝叶斯理论在目标跟踪领域的开放问题和未来研究方向.
    1)  本文责任编委 穆朝絮
  • 图  1  序贯处理方式示意图

    Fig.  1  The diagram of the sequential processing

    图  2  TBD框架示意图

    Fig.  2  The diagram of TBD

    图  3  JCE框架示意图

    Fig.  3  The diagram of JCE

    图  4  JAE框架示意图

    Fig.  4  The diagram of JAE

    图  5  JDE框架示意图

    Fig.  5  The diagram of JDE

    图  6  VB原理示意图[88]

    Fig.  6  The diagram of VB principle[88]

    图  7  EM原理示意图[88]

    Fig.  7  The diagram of EM principle[88]

    图  8  OTHR联合检测与跟踪概率图模型[120]

    Fig.  8  The probability graph model of joint detection and tracking of OTHR[120]

    图  9  多路径多模式多目标跟踪的联合优化框架[120]

    Fig.  9  The joint optimization framework of multi-path multi-mode multi-targat[120]

    表  1  研究背景统计比例表

    Table  1  Statistical proportion of research backgrounds

    研究背景 军事应用 民事应用 理论及综合类
    比例(%) 2015年 38.65 25.18 36.17
    2016年 26.10 39.70 34.20
    2017年 38.15 32.22 39.63
    下载: 导出CSV

    表  2  数学工具统计比例表

    Table  2  Statistical proportion of mathematics tools

    研究背景 概率论 随机集 证据推理 神经网络和
    机器学习
    其他
    比例(%) 2015年 47.72 8.07 7.72 9.82 26.67
    2016年 48.2 8.2 9.1 11.4 23.1
    2017年 51.95 11.72 7.43 14.45 14.45
    下载: 导出CSV
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  • 收稿日期:  2018-01-12
  • 录用日期:  2018-05-07
  • 刊出日期:  2019-07-20

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