高级搜索

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

考虑坐标耦合的三维变结构多模型机动目标跟踪方法

张宏伟 高志坚 张翊

张宏伟, 高志坚, 张翊. 考虑坐标耦合的三维变结构多模型机动目标跟踪方法[J]. 电子与信息学报, 2024, 46(8): 3267-3275. doi: 10.11999/JEIT231290
引用本文: 张宏伟, 高志坚, 张翊. 考虑坐标耦合的三维变结构多模型机动目标跟踪方法[J]. 电子与信息学报, 2024, 46(8): 3267-3275. doi: 10.11999/JEIT231290
ZHANG Hongwei, GAO Zhijian, ZHANG Yi. 3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3267-3275. doi: 10.11999/JEIT231290
Citation: ZHANG Hongwei, GAO Zhijian, ZHANG Yi. 3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3267-3275. doi: 10.11999/JEIT231290

考虑坐标耦合的三维变结构多模型机动目标跟踪方法

doi: 10.11999/JEIT231290 cstr: 32379.14.JEIT231290
基金项目: 中山大学青年培育项目(20lgpy72),中国科学院空间精密测量重点实验室开放基金(SPMT2022001),广东省高等学校科技创新(重点)项目(2020ZDZX1054)
详细信息
    作者简介:

    张宏伟:女,讲师,博士,研究方向为目标跟踪、智能信息处理

    高志坚:男,硕士,实验师,研究方向为机器视觉、传感器网络等

    张翊:女,博士生, 研究方向为视觉目标跟踪等

    通讯作者:

    张宏伟 zhanghw69@mail.sysu.edu.cn

  • 中图分类号: TN953

3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking

Funds: Sun Yat-sen University Youth Cultivation Project (20lgpy72), The Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology (SPMT2022001), The Key Project of DEGP (2020ZDZX1054)
  • 摘要: 在3维空间机动目标跟踪过程中,目标运动先验未知和坐标耦合误差会引起运动模型-模式失配,而模型-模式失配会引起状态估计有偏。该文根据目标运动速度正交条件修正状态转移矩阵,利用原始-对偶正则约束空间测量到球面可行域,结合自适应转弯率模型和无迹卡尔曼滤波(UKF),进行模型状态滤波并融合状态估计的一致输出,推导3维变结构多模型无迹卡尔曼滤波(VSMMUKF)算法。实验结果表明,相比多模重要性无迹卡尔曼滤波(MIUKF)算法,VSMMUKF计算量相当,能够更准确地拟合3维空间点目标机动运动。相比于交互多模型最大最小粒子滤波(IMM-MPF)算法,VSMMUKF跟踪固定翼无人机(UAV)的滤波精度提升了2.8%~59.9%,整体算法负担减小了1个数量级。
  • 图  1  3维坐标系下目标运动的速度正交几何关系

    图  2  点目标运动的航迹预测

    图  3  小型固定翼无人机试飞场景

    图  4  3种跟踪算法滤波性能比较

    表  1  表100轮蒙特卡罗实验统计滤波误差(均值、最大值及协方差)和实验运行时间

    算法 位置均方根误差 $ x $轴上滤波误差 $ y $轴上滤波误差 $ z $轴上滤波误差 运行时间 (s)
    均值 (m) 标准差 (m) 最大值 (m) 标准差 (m) 最大值 (m) 标准差 (m) 最大值 (m) 标准差 (m2)
    MIUKF 18.34 10.10 –25.41 10.19 27.28 10.70 17.04 5.42 1.36
    IMM-MPF 9.69 4.68 –14.05 4.94 16.63 6.06 8.36 2.18 16.05
    VSMUKF 3.88 2.05 –5.45 2.24 6.97 2.66 –5.11 2.12 5.93
    下载: 导出CSV
  • [1] BAR-SHALOM Y, LI X R, and KIRUBARAJAN T. Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software[M]. New York: John Wiley & Sons, 2002.
    [2] LI X R and JILKOV V P. Survey of maneuvering target tracking. Part I. Dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333–1364. doi: 10.1109/TAES.2003.1261132.
    [3] ZHANG Hongwei and LI Pengfei. Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking[J]. Chinese Journal of Aeronautics, 2023, 36(8): 313–330. doi: 10.1016/j.cja.2023.03.007.
    [4] 周宏仁. 机动目标跟踪[M]. 北京: 国防工业出版社, 1991.

    ZHOU Hongren. Tracking of Maneuvering Targets[M]. Beijing: National Defense Industry Press, 1991.
    [5] KIRUBARAJAN T and BAR-SHALOM Y. Kalman filter vs. IMM estimator: When do we need the latter?[C]. SPIE 4048, Signal and Data Processing of Small Targets 2000, Orlando, USA, 2000: 576–582. doi: 10.1117/12.392013.
    [6] 李盈萱, 王中训, 董云龙. 两种新的机动目标仿真模型[J]. 系统仿真学报, 2023, 35(7): 1581–1589. doi: 10.16182/j.issn1004731x.joss.22-0295.

    LI Yingxuan, WANG Zhongxun, and DONG Yunlong. Two new maneuvering target simulation methods[J]. Journal of System Simulation, 2023, 35(7): 1581–1589. doi: 10.16182/j.issn1004731x.joss.22-0295.
    [7] 滕康, 周勇. 基于当前统计模型改进的机动目标自适应跟踪算法[J/OL]. 现代雷达, 2023: 1–9. http://kns.cnki.net/kcms/detail/32.1353.TN.20231031.1535.002.html, 2024.

    TENG Kang and ZHOU Yong. Adaptive tracking algorithm of maneuvering target based on current statistical model[J]. Modern Radar, 2023: 1–9. http://kns.cnki.net/kcms/detail/32.1353.TN.20231031.1535.002.html, 2024.
    [8] 刘宗香, 谢维信, 黄敬雄. 一种用于三维空间杂波环境机动目标跟踪的数据互联方法[J]. 电子与信息学报, 2009, 31(4): 848–852. doi: 10.3724/SP.J.1146.2007.01880.

    LIU Zongxiang, XIE Weixin, and HUANG Jingxiong. A data association method for maneuvering target tracking in three-dimensional space under the circumstance of clutter[J]. Journal of Electronics & Information Technology, 2009, 31(4): 848–852. doi: 10.3724/SP.J.1146.2007.01880.
    [9] LI X R and JILKOV V P. Survey of maneuvering target tracking. Part V. Multiple-model methods[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255–1321. doi: 10.1109/TAES.2005.1561886.
    [10] LI X R, JILKOV V P, and RU J. Multiple-model estimation with variable structure-part VI: Expected-mode augmentation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 853–867. doi: 10.1109/TAES.2005.1541435.
    [11] LI X R. Model-set sequence-conditioned estimation for variable-structure MM estimation[C]. SPIE 3373, Signal and Data Processing of Small Targets, Orlando, USA, 1998: 546−558. doi: 10.1117/12.324650.
    [12] LI X R. Multiple-model estimation with variable structure. II. Model-set adaptation[J]. IEEE Transactions on Automatic Control, 2000, 45(11): 2047–2060. doi: 10.1109/9.887626.
    [13] LI X R and ZHANG Youmin. Multiple-model estimation with variable structure. V. Likely-model set algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 448–466. doi: 10.1109/7.845222.
    [14] 孙照强, 王志贵, 孟飞, 等. 基于EKF及弹道方程的弹道目标跟踪滤波器设计[J]. 系统工程与电子技术, 2022, 44(10): 3207–3212. doi: 10.12305/j.issn.1001-506x.2022.10.25.

    SUN Zhaoqiang, WANG Zhigui, MENG Fei, et al. Ballistic target tracking filter design based on EKF and ballistic equations[J]. Systems Engineering and Electronics, 2022, 44(10): 3207–3212. doi: 10.12305/j.issn.1001-506x.2022.10.25.
    [15] ZHANG Hongwei and XIE Weixin. Constrained unscented Kalman filtering for bearings-only maneuvering target tracking[J]. Chinese Journal of Electronics, 2020, 29(3): 501–507. doi: 10.1049/cje.2020.02.006.
    [16] JULIER S J, UHLMANN J K, and DURRANT-WHYTE H F. A new approach for filtering nonlinear systems[C]. 1995 American Control Conference - ACC’95, Seattle, USA, 1995: 1628–1632. doi: 10.1109/ACC.1995.529783.
    [17] 王平波, 刘杨. 基于改进自适应IMM-UKF算法的水下目标跟踪[J]. 电子与信息学报, 2022, 44(6): 1999–2005. doi: 10.11999/jeit211128.

    WANG Pingbo and LIU Yang. Underwater target tracking algorithm based on improved adaptive IMM-UKF[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1999–2005. doi: 10.11999/jeit211128.
    [18] LIM J, KIM H S, and PARK H M. Interactive-multiple-model algorithm based on Minimax particle filtering[J]. IEEE Signal Processing Letters, 2019, 27: 36–40. doi: 10.1109/LSP.2019.2954000.
    [19] JULIER S J and UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401–422. doi: 10.1109/JPROC.2003.823141.
    [20] 张宏伟, 张小虎, 曹勇. 贝叶斯序贯重要性积分滤波器[J]. 电子学报, 2022, 50(4): 823–831. doi: 10.12263/DZXB.20210716.

    ZHANG Hongwei, ZHANG Xiaohu, and CAO Yong. Bayesian sequential importance quadrature filter[J]. Acta Electronica Sinica, 2022, 50(4): 823–831. doi: 10.12263/DZXB.20210716.
    [21] LIM J, KIM H S, and PARK H M. Minimax particle filtering for tracking a highly maneuvering target[J]. International Journal of Robust and Nonlinear Control, 2020, 30(2): 636–651. doi: 10.1002/rnc.4785.
    [22] 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[J]. 自动化学报, 2021, 47(7): 1548–1557. doi: 10.16383/j.aas.c190091.

    WANG Yuqi, LU Zhou, and CAI Yunze. Consensus-based distributed variable structure multiple model[J]. Acta Automatica Sinica, 2021, 47(7): 1548–1557. doi: 10.16383/j.aas.c190091.
    [23] ROSSI F, VAN BEEK P, and WALSH T. Constraint programming[J]. Foundations of Artificial Intelligence, 2018, 3: 181–211.
    [24] XU Linfeng, LI X R, LIANG Yan, et al. Modeling and state estimation of linear destination-constrained dynamic systems[J]. IEEE Transactions on Signal Processing, 2022, 70: 2374–2387. doi: 10.1109/TSP.2022.3166113.
    [25] AFTAB W and MIHAYLOVA L. A learning gaussian process approach for maneuvering target tracking and smoothing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(1): 278–292. doi: 10.1109/TAES.2020.3021220.
    [26] ZHANG Hongwei, YE Xiaoyu, and HU Qi. Spatiotemporal learning via mixture importance Gaussian filtering with sparse regularization[J]. IEEE Signal Processing Letters, 2023, 30: 279–283. doi: 10.1109/LSP.2023.3258861.
    [27] FAN Xuxiang, WANG Gang, HAN Jiachen, et al. Interacting multiple model based on maximum correntropy Kalman filter[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 68(8): 3017–3021. doi: 10.1109/TCSII.2021.3068221.
    [28] ZHANG Hongwei. Multiple importance unscented Kalman filtering with soft spatiotemporal constraint for multi‐passive‐sensor target tracking[J]. International Journal of Robust and Nonlinear Control, 2023, 33(1): 264–281. doi: 10.1002/rnc.5977.
    [29] SÄRKKÄ S. Bayesian Filtering and Smoothing[M]. Cambridge: Cambridge University Press, 2013. doi: 10.1017/CBO9781139344203.
    [30] SILVESTRE D. Constrained convex generators: A tool suitable for set-based estimation with range and bearing measurements[J]. IEEE Control Systems Letters, 2021, 6: 1610–1615. doi: 10.1109/LCSYS.2021.3129729.
    [31] BOYD S, BOYD S P, and VANDENBERGHE L. Convex Optimization[M]. Cambridge, U. K. : Cambridge University Press, 2004.
  • 加载中
图(4) / 表(1)
计量
  • 文章访问数:  149
  • HTML全文浏览量:  40
  • PDF下载量:  23
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-11-21
  • 修回日期:  2024-04-17
  • 网络出版日期:  2024-05-13
  • 刊出日期:  2024-08-30

目录

    /

    返回文章
    返回