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CBMeMBer滤波器序贯蒙特卡罗实现新方法的研究

陈辉 韩崇昭

陈辉, 韩崇昭. CBMeMBer滤波器序贯蒙特卡罗实现新方法的研究. 自动化学报, 2016, 42(1): 26-36. doi: 10.16383/j.aas.2016.c150182
引用本文: 陈辉, 韩崇昭. CBMeMBer滤波器序贯蒙特卡罗实现新方法的研究. 自动化学报, 2016, 42(1): 26-36. doi: 10.16383/j.aas.2016.c150182
CHEN Hui, HAN Chong-Zhao. A New Sequential Monte Carlo Implementation of Cardinality Balanced Multi-target Multi-Bernoulli Filter. ACTA AUTOMATICA SINICA, 2016, 42(1): 26-36. doi: 10.16383/j.aas.2016.c150182
Citation: CHEN Hui, HAN Chong-Zhao. A New Sequential Monte Carlo Implementation of Cardinality Balanced Multi-target Multi-Bernoulli Filter. ACTA AUTOMATICA SINICA, 2016, 42(1): 26-36. doi: 10.16383/j.aas.2016.c150182

CBMeMBer滤波器序贯蒙特卡罗实现新方法的研究

doi: 10.16383/j.aas.2016.c150182
基金项目: 

国家自然科学基金创新研究群体 61221063

甘肃省高等学校科研项目 2014A-035

国家自然科学基金 61370037, 61005026, 61473217

国家重点基础研究发展计划(973计划) 2013CB329405

详细信息
    作者简介:

    韩崇昭 西安交通大学电子与信息工程学院教授.主要研究方向为多源信息融合,随机控制与自适应控制,非线性频谱分析.E-mail:czhan@mail.xjtu.edu.cn

    通讯作者:

    陈辉 西安交通大学电子与信息工程学院综合自动化研究所博士研究生.主要研究方向为目标跟踪.本文通信作者.E-mail:huich78@hotmail.com

A New Sequential Monte Carlo Implementation of Cardinality Balanced Multi-target Multi-Bernoulli Filter

Funds: 

Foundation for Innovative Research Groups of the National Natural Science Foundation of China 61221063

and Foundation of Higher Education of Gansu Province 2014A-035

National Natural Science Foundation of China 61370037, 61005026, 61473217

Supported by National Basic Research Program of China(973 Program) 2013CB329405

More Information
    Author Bio:

    Professor at the School of Electronic and Information Engineering, Xi' an Jiaotong University. His research interest covers multi-source information fusion, stochastic control and adaptive control, and nonlinear spectral analysis

    Corresponding author: CHEN Hui Ph.D. candidate at the Institute of Integrated Automation, School of Electronic and Information Engineering, Xi' an Jiaotong University. His main research interest is target tracking. Corresponding author of this paper
  • 摘要: 为提升多伯努利滤波器序贯蒙特卡罗(Sequential Monte Carlo, SMC)实现中粒子采样的有效性,提出一种CBMeMBer辅助粒子滤波(Auxiliary particle filter, APF)实现的新方法.首先,利用多伯努利后验概率密度选择适合于CBMeMBer滤波器的辅助变量去重新定义采样问题.分别选择量测和先验密度分量作为辅助变量,确保最终的状态粒子能够集中在真实目标量测对应航迹的伯努利概率密度上进行采样,以使粒子向似然函数的峰值区移动,得到更为精确的多目标多伯努利(Multi-target multi-Bernoulli, MeMBer)后验概率密度的估计.同时,文中深入研究并给出了在量测更新和漏检情况下辅助变量以及多目标状态采样分布函数的设计,并研究利用渐近更新(Progressive correction, PC)算法对先验密度分量的量测更新进行迭代逼近计算,以提高最终分布函数求解的准确度.最后,针对两个典型非线性多目标跟踪问题的应用验证了算法的有效性.
  • 图  1  实际目标的轨迹

    Fig.  1  Actual target trajectories

    图  2  传统SMC-CBMeMBer滤波器的目标跟踪效果

    Fig.  2  Target tracking with the traditional SMC-CBMeMBer filter

    图  3  本文算法的目标跟踪效果

    Fig.  3  Target tracking with the proposed filter

    图  4  多目标位置估计OSPA的比较

    Fig.  4  Tracking performance comparison for position OSPA

    图  5  目标个数估计的比较

    Fig.  5  Tracking performances of target number estimations

    图  6  传感器轨迹

    Fig.  6  Sensor trajectory

    图  7  传统SMC-CBMeMBer滤波器的BOT跟踪效果

    Fig.  7  Bearings-only tracking with the traditional SMC-CBMeMBer filter

    图  8  本文算法的BOT跟踪效果

    Fig.  8  Bearings-only tracking with the proposed filter

    图  9  多目标位置估计OSPA的比较

    Fig.  9  Tracking performance comparison for position OSPA

    图  10  目标个数估计的比较

    Fig.  10  Tracking performances of target number estimations

    表  1  不同采样规模下的性能比较

    Table  1  Tracking performance versus sampling size

    $L_s$ 100 300 500 1000 1500
    BFOSPA~(m) 32.43 23.64 20.53 17.42 17.04
    时间(s) 0.41 1.78 3.057.31 11.31
    APFOSPA(m) 17.18 16.41 16.26 16.18 16.12
    时间(s) 1.27 4.19 7.23 18.78 33.70
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-04-08
  • 录用日期:  2015-10-19
  • 刊出日期:  2016-01-01

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