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一种基于模型概率单调性变化的自适应IMM-UKF改进算法

王平波 陈强 卫红凯 贾耀君 沙浩然

王平波, 陈强, 卫红凯, 贾耀君, 沙浩然. 一种基于模型概率单调性变化的自适应IMM-UKF改进算法[J]. 电子与信息学报, 2024, 46(1): 41-48. doi: 10.11999/JEIT230380
引用本文: 王平波, 陈强, 卫红凯, 贾耀君, 沙浩然. 一种基于模型概率单调性变化的自适应IMM-UKF改进算法[J]. 电子与信息学报, 2024, 46(1): 41-48. doi: 10.11999/JEIT230380
WANG Pingbo, CHEN Qiang, WEI Hongkai, JIA Yaojun, SHA Haoran. Improved Adaptive IMM-UKF Algorithm Based on Monotonous Transformation of Model Probability[J]. Journal of Electronics & Information Technology, 2024, 46(1): 41-48. doi: 10.11999/JEIT230380
Citation: WANG Pingbo, CHEN Qiang, WEI Hongkai, JIA Yaojun, SHA Haoran. Improved Adaptive IMM-UKF Algorithm Based on Monotonous Transformation of Model Probability[J]. Journal of Electronics & Information Technology, 2024, 46(1): 41-48. doi: 10.11999/JEIT230380

一种基于模型概率单调性变化的自适应IMM-UKF改进算法

doi: 10.11999/JEIT230380
详细信息
    作者简介:

    王平波:男,博士,教授,研究方向为水声信号处理、声呐作战使用、目标定位跟踪等

    陈强:男,硕士生,研究方向为水声信号处理、目标定位跟踪

    卫红凯:男,博士,讲师,研究方向为水声信号处理、目标定位跟踪

    贾耀君:男,博士生,研究方向为主动声呐信号与信息处理

    沙浩然:男,本科生,研究方向为目标定位跟踪

    通讯作者:

    卫红凯 whk200605@163.com

  • 中图分类号: TN929.3; TN953

Improved Adaptive IMM-UKF Algorithm Based on Monotonous Transformation of Model Probability

  • 摘要: 针对现有交互式多模型(IMM)算法模型间切换迟滞和转换速率慢的缺点,提出一种基于模型概率单调性变化的自适应交互式多模型无迹卡尔曼滤波改进算法(mIMM-UKF)。该算法利用后验信息模型概率的单调性,对马尔可夫转移概率矩阵及模型估计概率进行二次修正,加快了匹配模型的切换速度及转换速率。仿真结果表明,与现有算法相比,该算法通过快速切换匹配模型,有效提高了水下目标跟踪精度。
  • 图  1  IMM-UKF算法流程图

    图  2  CA-CV-CT轨迹3种算法跟踪轨迹对比

    图  3  CA-CV-CT轨迹3种算法均方根误差对比

    图  4  CA-CV-CT轨迹3种算法模型概率对比

    图  5  CV-CT(左)-CT(右)轨迹3种算法跟踪轨迹对比

    图  6  CV-CT(左)-CT(右)轨迹3种算法均方根误差对比

    图  7  CV-CT(左)-CT(右)3种算法模型概率对比

    表  1  CA-CV-CT轨迹3种算法ARMSE对比

    算法位置ARMSE(m)速度ARMSE(m/s)
    本文算法77.723.8
    文献[14]算法82.704.1
    文献[15]算法80.843.9
    下载: 导出CSV

    表  2  CV-CT(左)-CT(右)3种算法ARMSE对比

    算法位置ARMSE(m)速度ARMSE(m/s)
    本文算法14.301.27
    文献[14]算法16.351.43
    文献[15]算法14.971.33
    下载: 导出CSV
  • [1] MAZOR E, AVERBUCH A, BAR-SHALOM Y, et al. Interacting multiple model methods in target tracking: A survey[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1): 103–123. doi: 10.1109/7.640267
    [2] LI Yi, CHEN Xinhua, and SUN Changyu. A tracking method based on particle filter for multistatic sonar system[C]. Proceedings of 2014 IEEE International Conference on Signal Processing, Communications and Computing, Guilin, China, 2014: 162–165.
    [3] FOO P H. Combining the interacting multiple model method with particle filters for manoeuvring target tracking with a multistatic radar system[J]. IET Radar, Sonar & Navigation, 2011, 5(7): 697–706. doi: 10.1049/iet-rsn.2010.0357
    [4] 张文, 孙瑞胜. EKF与UKF的性能比较及应用[J]. 南京理工大学学报, 2015, 39(5): 614–618. doi: 10.14177/j.cnki.32-1397n.2015.39.05.017

    ZHANG Wen and SUN Ruisheng. Research on performance comparison of EKF and UKF and their application[J]. Journal of Nanjing University of Science and Technology, 2015, 39(5): 614–618. doi: 10.14177/j.cnki.32-1397n.2015.39.05.017
    [5] 万莉, 刘焰春, 皮亦鸣. EKF、UKF、PF目标跟踪性能的比较[J]. 雷达科学与技术, 2007, 5(1): 13–16. doi: 10.3969/j.issn.1672-2337.2007.01.003

    WAN Li, LIU Yanchun, and PI Yiming. Comparing of target-tracking performances of EKF, UKF and PF[J]. Radar Science and Technology, 2007, 5(1): 13–16. doi: 10.3969/j.issn.1672-2337.2007.01.003
    [6] JULIER S J and UHLMANN J. The scaled unscented transformation (Author’s Comments)[J]. IEEE Transactions on Automatic Control, 2002, 47: 1408–1409. doi: 10.1109/TAC.2002.800741
    [7] 仇世刚, 汪圣利. 模型转移概率自适应的交互式多模型UKF算法[J]. 自动化技术与应用, 2008, 27(6): 61–66,56. doi: 10.3969/j.issn.1003-7241.2008.06.017

    QIU Shigang and WANG Shengli. An interacting multiple model UKF algorithm with adaptive Markov transition probabilities[J]. Techniques of Automation and Applications, 2008, 27(6): 61–66,56. doi: 10.3969/j.issn.1003-7241.2008.06.017
    [8] 苗伟, 李昌玺, 吴聪. 基于修正转弯模型的交互多模型跟踪算法[J]. 现代防御技术, 2015, 43(3): 113–118. doi: 10.3969/j.issn.1009-086X.2015.03.021

    MIAO Wei, LI Changxi, and WU Cong. Interactive multiple model tracking algorithm based on the modified model of turning[J]. Modern Defense Technology, 2015, 43(3): 113–118. doi: 10.3969/j.issn.1009-086X.2015.03.021
    [9] 马天力, 张扬, 高嵩, 等. 具有噪声信息与状态模型不确定系统的IMM自适应滤波[J]. 控制与决策, 2023.

    MA Tianli, ZHANG Yang, GAO Song, et al. Interactive multiple model adaptive filter for system with uncertain state model and noise information[J]. Control and Decision, 2023.
    [10] 李明, 柴洪洲, 靳凯迪, 等. 改进的强跟踪自适应UKF算法及其在大方位失准角对准中的应用[J]. 导航定位学报, 2022, 10(6): 165–172. doi: 10.3969/j.issn.2095-4999.2022.06.022

    LI Ming, CHAI Hongzhou, JIN Kaidi, et al. Improved strong tracking adaptive UKF algorithm and its application in large azimuth misalignment[J]. Journal of Navigation and Positioning, 2022, 10(6): 165–172. doi: 10.3969/j.issn.2095-4999.2022.06.022
    [11] ZHU Hongfeng, XIONG Wei, and CUI Yaqi. An adaptive interactive multiple-model algorithm based on end-to-end learning[J]. Chinese Journal of Electronics, 2023, 32(4): 1–13. doi: 10.23919/cje.2021.00.442
    [12] 李昊润, 卜凡康, 周剑雄. 修正的马尔科夫转移矩阵自适应IMM算法[J]. 火力与指挥控制, 2021, 46(9): 118–124,132. doi: 10.3969/j.issn.1002-0640.2021.09.021

    LI Haorun, BU Fankang, and ZHOU Jianxiong. Improved adaptive Markov matrix IMM algorithm[J]. Fire Control &Command Control, 2021, 46(9): 118–124,132. doi: 10.3969/j.issn.1002-0640.2021.09.021
    [13] 封普文, 黄长强, 曹林平, 等. 马尔可夫矩阵修正IMM跟踪算法[J]. 系统工程与电子技术, 2013, 35(11): 2269–2274. doi: 10.3969/j.issn.1001-506X.2013.11.07

    FENG Puwen, HUANG Changqiang, CAO Linping, et al. Research on adaptive Markov matrix IMM tracking algorithm[J]. Systems Engineering and Electronics, 2013, 35(11): 2269–2274. doi: 10.3969/j.issn.1001-506X.2013.11.07
    [14] 叶瑾, 许枫, 杨娟, 等. 一种改进的时变转移概率AIMM跟踪算法[J]. 应用声学, 2020, 39(2): 246–252. doi: 10.11684/j.issn.1000-310X.2020.02.011

    YE Jin, XU Feng, YANG Juan, et al. An improved AIMM tracking algorithm based on adaptive transition probability[J]. Journal of Applied Acoustics, 2020, 39(2): 246–252. doi: 10.11684/j.issn.1000-310X.2020.02.011
    [15] 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
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  • 被引次数: 0
出版历程
  • 收稿日期:  2023-05-05
  • 修回日期:  2023-07-17
  • 网络出版日期:  2023-07-21
  • 刊出日期:  2024-01-17

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