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基于GLMB滤波和Gibbs采样的多扩展目标有限混合建模与跟踪算法

陈一梅 刘伟峰 孔明鑫 张桂林

陈一梅, 刘伟峰, 孔明鑫, 张桂林. 基于GLMB滤波和Gibbs采样的多扩展目标有限混合建模与跟踪算法. 自动化学报, 2020, 46(7): 1445-1456. doi: 10.16383/j.aas.c180077
引用本文: 陈一梅, 刘伟峰, 孔明鑫, 张桂林. 基于GLMB滤波和Gibbs采样的多扩展目标有限混合建模与跟踪算法. 自动化学报, 2020, 46(7): 1445-1456. doi: 10.16383/j.aas.c180077
CHEN Yi-Mei, LIU Wei-Feng, KONG Ming-Xin, ZHANG Gui-Lin. A Modeling and Tracking Algorithm of Finite Mixture Models for Multiple Extended Target Based on the GLMB Filter and Gibbs Sampler. ACTA AUTOMATICA SINICA, 2020, 46(7): 1445-1456. doi: 10.16383/j.aas.c180077
Citation: CHEN Yi-Mei, LIU Wei-Feng, KONG Ming-Xin, ZHANG Gui-Lin. A Modeling and Tracking Algorithm of Finite Mixture Models for Multiple Extended Target Based on the GLMB Filter and Gibbs Sampler. ACTA AUTOMATICA SINICA, 2020, 46(7): 1445-1456. doi: 10.16383/j.aas.c180077

基于GLMB滤波和Gibbs采样的多扩展目标有限混合建模与跟踪算法

doi: 10.16383/j.aas.c180077
基金项目: 

国家自然科学基金 61771177

国家自然科学基金 61333011

江苏省自然科学基金项目 BK20160148

杭州电子科技大学优秀学位论文培育基金项目 yxlw2018008

详细信息
    作者简介:

    陈一梅  杭州电子科技大学自动化学院系统科学与控制工程研究所硕士研究生.主要研究方向为目标跟踪, 信息融合.E-mail: chenym245600@163.com

    孔明鑫  杭州电子科技大学自动化学院系统科学与控制工程研究所硕士研究生.主要研究方向为深度学习, 目标识别.E-mail: 171060021@hdu.edu.cn

    张桂林  中国电子科技集团公司第二十八研究所研究员.主要研究方向为为目标跟踪, 不确定信息处理, 模式识别. E-mail: 1917907284@qq.com

    通讯作者:

    刘伟峰  杭州电子科技大学副教授.主要研究方向为目标跟踪, 不确定信息处理, 模式识别.本文通信作者. E-mail: liuwf@hdu.edu.cn

A Modeling and Tracking Algorithm of Finite Mixture Models for Multiple Extended Target Based on the GLMB Filter and Gibbs Sampler

Funds: 

National Nature Science Foundation of China 61771177

National Nature Science Foundation of China 61333011

Natural Science Foundation for Young Scientists of Jiangsu Province BK20160148

Foundation for Hangzhou Dianzi University Excellent Dissertation Cultivation Project yxlw2018008

More Information
    Author Bio:

    CHEN Yi-Mei  Master student at the Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University. Her research interest covers target tracking and information fusion

    KONG Ming-Xin  Master student at the Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University. Her research interest covers deep learning and target identification

    ZHANG Gui-Lin  Professor at the 28th research institute of China electronics technology group corporation. His research interest covers target tracking, uncertain information processing, and pattern recognition

    Corresponding author: LIU Wei-Feng  Associate professor at Hangzhou Dianzi University. His research interest covers target tracking, uncertain information processing, and pattern recognition. Corresponding author of this paper
  • 摘要: 本文针对杂波条件下多扩展目标的状态估计, 目标个数估计, 扩展目标形状估计问题, 提出了一种基于标签随机有限集(Labelled random finite sets, L-RFS)框架下多扩展目标跟踪学习算法, 该学习算法主要包括两方面:多扩展目标动态建模和多扩展目标的跟踪估计.首先, 结合广义标签多伯努利滤波器(Generalized labelled multi-Bernoulli, GLMB)建立了扩展目标的量测有限混合模型(Finite mixture models, FMM), 利用Gibbs采样和贝叶斯信息准则(Bayesian information criterion, BIC)准则推导出有限混合模型的参数来对多扩展目标形状进行学习, 然后采用等效量测方法来替代扩展目标产生的量测, 对扩展目标形状采用椭圆逼近建模, 实现扩展目标形状与状态的估计.仿真实验表明本文所给的方法能够有效跟踪多扩展目标, 并且在目标个数估计方面优于CBMeMBer算法.此外, 与标签多伯努利滤波(LMB)计算比较表明: GLMB和LMB算法滤波估计精度接近, 二者精度高于CBMeMBer算法.
    Recommended by Associate Editor XU Bin
    1)  本文责任编委 许斌
  • 图  1  扩展目标("四角星"表示量测)

    Fig.  1  The concept map of extended target (the four-pointed star denotes the measurement)

    图  2  多扩展目标运动真实轨迹

    Fig.  2  Ground truths for multiple extended targets

    图  3  由GLMB滤波器得到的轨迹估计

    Fig.  3  Track estimation by GLMB filter

    图  4  由CBMeMBer滤波器得到的轨迹估计

    Fig.  4  Track estimation by CBMeMBer filter

    图  5  由GLMB滤波算法得到状态估计

    Fig.  5  The state estimation by GLMB filter

    图  6  多扩展目标个数估计对比

    Fig.  6  The cardinality estimates comparison for multiple extended targets

    图  7  OSPA距离(100 MC runs)

    Fig.  7  OSPA distance (100 MC runs)

    图  8  平行运动场景

    Fig.  8  Parallel motion scenes

    图  9  交叉运动场景

    Fig.  9  Cross-motion scenes

    图  10  平行运动场景下状态估计

    Fig.  10  State estimation in parallel motion scenes

    图  11  交叉运动场景下状态估计

    Fig.  11  State estimation in cross-motion scenarios

    图  12  平行运动和交叉运动场景下的扩展目标个数估计

    Fig.  12  Estimated number of extended targets in cross-motion and parallel motion scenarios

    图  13  交叉运动和平行运动场景OSPA距离(100 MCs)

    Fig.  13  OSPA distance of cross-motion and parallel motion scenes (100 MCs)

    表  1  算法性能分析

    Table  1  Performance analysis of algorithms

    线性 线性
    时间(秒/步) 1.37 0.05
    目标个数估计准确率 97.8 % 84.6 %
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-31
  • 录用日期:  2018-07-15
  • 刊出日期:  2020-07-24

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