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变分贝叶斯概率数据关联算法

恽鹏 吴盘龙 李星秀 何山

恽鹏, 吴盘龙, 李星秀, 何山. 变分贝叶斯概率数据关联算法. 自动化学报, 2020, 41(x): 1−10 doi: 10.16383/j.aas.c200407
引用本文: 恽鹏, 吴盘龙, 李星秀, 何山. 变分贝叶斯概率数据关联算法. 自动化学报, 2020, 41(x): 1−10 doi: 10.16383/j.aas.c200407
Yun Peng, Wu Pan-Long, Li Xing-Xiu, He Shan. Variational bayesian probabilistic data association algorithm. Acta Automatica Sinica, 2020, 41(x): 1−10 doi: 10.16383/j.aas.c200407
Citation: Yun Peng, Wu Pan-Long, Li Xing-Xiu, He Shan. Variational bayesian probabilistic data association algorithm. Acta Automatica Sinica, 2020, 41(x): 1−10 doi: 10.16383/j.aas.c200407

变分贝叶斯概率数据关联算法

doi: 10.16383/j.aas.c200407
基金项目: 国家自然科学基金(61473153), 航空科学基金(2016ZC59006)资助
详细信息
    作者简介:

    恽鹏:南京理工大学自动化学院博士研究生.主要研究方向为目标跟踪, 鲁棒滤波. E-mail: yunpeng0409@163.com

    吴盘龙:南京理工大学自动化学院教授.主要研究方向为目标跟踪, 信号处理, 火控系统. 本文通信作者. E-mail: plwu@163.com

    李星秀:南京理工大学理学院副教授.主要研究方向为目标跟踪, 信号处理. E-mail: xxlwpl@126.com

    何山:南京理工大学自动化学院博士研究生.主要研究方向为目标跟踪, 火控系统. E-mail: heshanhshs@163.com

Variational Bayesian Probabilistic Data Association Algorithm

Funds: Supported by National Natural Science Foundation of China (61473153), Aeronautical Science Foundation of China (2016ZC59006)
  • 摘要: 针对杂波环境下的目标跟踪问题, 提出了一种基于变分贝叶斯的概率数据关联算法(VB-PDA). 该算法首先将关联事件视为一个随机变量并利用多项分布对其进行建模, 随后基于数据集、目标状态、关联事件的联合概率密度函数求取关联事件的后验概率密度函数, 最后将关联事件的后验概率密度函数引入变分贝叶斯框架中以获取状态近似后验概率密度函数. 相比于概率数据关联算法, VB-PDA算法在提高算法实时性的同时在权重KL平均准则下获取了近似程度更高的状态后验概率密度函数. 相关仿真实验对提出算法的有效性进行了验证.
  • 图  1  场景1下三种算法的位置RSME

    Fig.  1  The RMSE of position from three algorithms in scenario 1

    图  2  仿真场景1下三种算法的速度RSME

    Fig.  2  The RMSE of velocity from three algorithms in scenario 1

    图  3  仿真场景2下三种算法的位置RSME

    Fig.  3  The RMSE of position from three algorithms in scenario 2

    图  4  仿真场景2下三种算法的速度RSME

    Fig.  4  The RMSE of velocity from three algorithms in scenario 2

    表  1  一步状态更新过程中所需的加减运算与乘除运算次数

    Table  1  The number of addition and subtraction operations and multiplication and division operations required in the process of one-step state update

    算法 加减法运算次数 乘除法运算次数
    PDA $\begin{aligned} &{r^3}{n_{k + 1} } + {r^2}(m{n_{k + 1} } - {n_{k + 1} } + 2) + \\ & r({n_{k + 1} } + 1 + 2m) + 2{n_{k + 1} } - 1 +\\ & m{n_{k + 1} } - m\end{aligned}$ $\begin{aligned} & {r^3} + {r^2}(2{n_{k + 1} } + m + 3) + \\ & r(2m + 1) + m{n_{k + 1} } + 1 \end{aligned}$
    DW-PDA $\begin{aligned} & {r^3}{n_{k + 1} } + {r^2}(m{n_{k + 1} } - {n_{k + 1} } + 2) + \\ & r({n_{k + 1} } + 1 + 2m) + 4{n_{k + 1} } - 3 + \\ & m{n_{k + 1} } - m\end{aligned}$ $\begin{aligned} &{r^3} + {r^2}(2{n_{k + 1} } + m + 3)+ \\ & r(2m + 1) + m{n_{k + 1} } + 4{n_{k + 1} } + 1\end{aligned}$
    VB-PDA $\begin{aligned} & {r^3}{n_{k + 1} } + {r^2}(m{n_{k + 1} } - 2{n_{k + 1} } + 1) + \\ & 2mr + 2{n_{k + 1} } - 1 + m{n_{k + 1} } - m \end{aligned}$ $\begin{aligned} & {r^3} + {r^2}m + r(2m + 1)+ \\ & m{n_{k + 1} } + 1 + {m^2} \end{aligned}$
    下载: 导出CSV

    表  2  场景1下三种算法的TRSME

    Table  2  The TRSME of three algorithms in scenario 1

    算法 位置TRMSE/m 速度TRMSE/(m/s)
    PDA 6.892 0.873
    DW-PDA 6.792 0.839
    VB-PDA 6.742 0.872
    下载: 导出CSV

    表  3  场景1下一次蒙特卡洛仿真实验所需的计算时间

    Table  3  The computational time of three algorithms at one Monte Carlo simulation experiment in scenario 1

    算法 计算时间/ms
    PDA 56.52
    DW-PDA 58.72
    VB-PDA 46.70
    下载: 导出CSV

    表  4  仿真场景2下三种算法的TRSME

    Table  4  The TRSME of three algorithms in scenario 2

    算法 位置TRMSE/m 速度TRMSE/(m/s)
    IMM-PDA 8.409 1.874
    IMM-DW-PDA 8.101 1.818
    IMM-VB-PDA 7.967 1.784
    下载: 导出CSV
  • [1] 孟琭, 杨旭. 目标跟踪算法综述. 自动化学报, 2019, 45(7): 1244−1260

    MENG Lu, YANG Xu. A Survey of Object Tracking Algorithms. Acta Automatica Sinica, 2019, 45(7): 1244−1260
    [2] Sobhani B, Paolini E, Giorgetti A. Target Tracking for UWB Multistatic Radar Sensor Networks. IEEE Journal of Selected Topics in Signal Processing, 2017, 8(1): 125−136
    [3] 甘林海, 王刚, 刘进忙, 李松. 群目标跟踪技术综述. 自动化学报, 2020, 46(3): 411−426

    GAN Lin-Hai, WANG Gang, LIU Jin-Mang, LI Song. An Overview of Group Target Tracking. Acta Automatica Sinica, 2020, 46(3): 411−426
    [4] 何友, 黄勇, 关键, 陈小龙. 海杂波中的雷达目标检测技术综述. 现代雷达, 2014, 36(12): 1−9

    HE You, HUANG Yong, GUAN Jian, CHEN Xiao -Long. An overview on radar target detection in sea clutter. Modern radar, 2014, 36(12): 1−9
    [5] 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

    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
    [6] Kirubarajan T, Bar-Shalom Y. Probabilistic data association techniques for target tracking in clutter. Proceedings of the IEEE, 2004, 92(3): 536−557 doi: 10.1109/JPROC.2003.823149
    [7] Wu P L, Zhou Y, Li X X. Space-based passive tracking of non-cooperative space target using robust filtering algorithm. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2016, 230(6): 551−561 doi: 10.1177/0959651816637770
    [8] Song T L, L D G, R J. A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment. Signal Processing, 2005, 85(10): 2044−2053 doi: 10.1016/j.sigpro.2005.01.016
    [9] Sinha A, Ding Z, Kirubarajan T, Farooq, M. Track Quality Based Multitarget Tracking Approach for Global Nearest-Neighbor Association. IEEE Transactions on Aerospace & Electronic Systems, 2012, 48(2): 1179−1191
    [10] Az iz, Ashraf M. A new nearest-neighbor association approach based on fuzzy clustering. Aerospace Science & Technology, 2013, 26(1): 87−97
    [11] Bar-Shalom Y, Tse E. Tracking in a cluttered environment with probabilistic data association. Automatica, 1975, 11(5): 451−460 doi: 10.1016/0005-1098(75)90021-7
    [12] Wu P L, Li X X, Kong J S, Liu J L. Heterogeneous multiple sensors joint tracking of maneuvering target in clutter. Sensors, 2015, 15(7): 17350−17365 doi: 10.3390/s150717350
    [13] 程婷, 何子述, 李亚星. 一种具有自适应关联门的杂波中机动目标跟踪算法. 电子与信息学报, 2012, 34(4): 865−870

    CHENG Ting, HE Zi-Shu, LI Ya-Xing. A maneuvering target tracking algorithm in clutter with adaptive correlation gate. Journal of Electronics and Information, 2012, 34(4): 865−870
    [14] Barshalom Y, Daum F, Huang J. The Probabilistic Data Association Filter. IEEE Control Systems Magazine, 2012, 29(6): 82−100
    [15] Kirubarajan T, Bar-Shalom Y, Blair W D, Watson G A. IMMPDAF for radar management and tracking benchmark with ECM. IEEE Transactions on Aerospace & Electronic Systems, 1998, 34(4): 1115−1134
    [16] 潘泉, 刘刚, 戴冠中, 张洪才. 联合交互式多模型概率数据关联算法. 航空学报, 1999, 20(3): 234−238

    PAN Quan, LIU Gang, DAI Guan-Zhong, ZHANG Hong-Cai. Combined interacting multiple models probabilistic data association algorithm. Acta Aeronautica ET Astronautica Sinica, 1999, 20(3): 234−238
    [17] 陈晓, 李亚安, 李余兴, 蔚婧. 基于距离加权的概率数据关联机动目标跟踪算法. 上海交通大学学报, 2018, 52(4): 100−105

    CHEN Xiao, LI Ya-An, Li Yu-Xing, WEI Jing. Maneuvering Target Tracking Algorithm Based on Weighted Distance of Probability Data Association. Journal of Shanghai Jiao Tong University, 2018, 52(4): 100−105
    [18] Yun P, Wu P L, He S. Pearson Type VⅡ Distribution-Based Robust Kalman Filter under Outliers Interference. IET Radar, Sonar & Navigation, 2019, 13(8): 1389−1399
    [19] Battistelli G, Chisci L. Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability. Automatica, 2014, 50(3): 707−718 doi: 10.1016/j.automatica.2013.11.042
    [20] Huang Y L, Zhang Y G, Li N, Wu Z M, Chambers J. A novel robust student's t-based Kalman filter. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3): 1545−1554 doi: 10.1109/TAES.2017.2651684
    [21] Panta K, Ba-Ngu V, Singh S. Novel data association schemes for the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 556−570 doi: 10.1109/TAES.2007.4285353
    [22] Blom H A P, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control, 1988, 33(8): 780−783 doi: 10.1109/9.1299
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  • 收稿日期:  2020-06-11
  • 录用日期:  2020-09-07

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