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时序信息驱动的并行交互式多模型水下目标跟踪算法

兰朝凤 张桐基 陈欢

兰朝凤, 张桐基, 陈欢. 时序信息驱动的并行交互式多模型水下目标跟踪算法[J]. 电子与信息学报. doi: 10.11999/JEIT250044
引用本文: 兰朝凤, 张桐基, 陈欢. 时序信息驱动的并行交互式多模型水下目标跟踪算法[J]. 电子与信息学报. doi: 10.11999/JEIT250044
LAN Chaofeng, ZHANG Tongji, CHEN Huan. Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250044
Citation: LAN Chaofeng, ZHANG Tongji, CHEN Huan. Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250044

时序信息驱动的并行交互式多模型水下目标跟踪算法

doi: 10.11999/JEIT250044 cstr: 32379.14.JEIT250044
基金项目: 国家自然科学基金(11804068),黑龙江省优秀青年教师基础研究支持计划(YQJH2024064)
详细信息
    作者简介:

    兰朝凤:女,教授,博士生导师,研究方向为水下声信号分析与处理、智能任务规划与决策等

    张桐基:男,硕士,研究方向为目标跟踪

    陈欢:男,高级工程师,研究方向为水下声信号探测与识别,大型航行体的智能路径规划与决策

    通讯作者:

    陈欢 13545022860@139.com

  • 中图分类号: TN713

Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking

Funds: The National Natural Science Foundation of China (11804068), Heilongjiang Provincial Outstanding Young Teachers Basic Research Support Programme (YQJH2024064)
  • 摘要: 随着水下目标运动形式的多样化和复杂化,现有的交互式多模型算法(IMM)在面对目标状态切换时存在模型切换缓慢及跟踪精度不足的问题。为此,该文在经典IMM算法的基础上,提出一种基于时序信息的并行交互式多模型目标跟踪算法(TIP-IMM)。该算法通过比较相邻时刻的模型概率变化趋势,动态修正状态转移矩阵的参数,再经归一化处理实现状态转移矩阵的自适应更新。同时利用并行IMM框架和信息熵来动态更新模型概率,避免因过度修正状态转移矩阵而导致的跟踪精度下降。仿真结果表明,与现有算法相比,所提算法对目标的预测精度提高了3.52%~7.87%。同时模型的切换速度更快,有效地提高了水下目标跟踪精度。
  • 图  1  TIP-IMM算法流程

    图  2  4种算法跟踪轨迹对比图

    图  3  4种算法的模型概率曲线变化图

    图  4  不同算法的位置误差曲线与时间关系

    图  5  不同算法算在X方向和Y方向上的误差曲线与时间关系

    表  1  目标运动情况

    时间(s)1~4041~7071~9091~115116~150
    运动状态直线运动匀速转弯 –1.2 °/s直线运动匀速转弯 1.8 °/s直线运动
    下载: 导出CSV

    表  2  4种算法的误差对比

    方法ARMSE
    位置误差(m)速度误差(m/s)
    位置整体误差X方向Y方向X方向Y方向
    经典IMM18.360011.454314.45561.04371.6767
    文献[18]17.637310.937213.96091.04081.6653
    文献[19]17.624810.919413.93021.03111.6653
    本文算法(TIP-IMM)17.024710.661413.39261.01901.6014
    下载: 导出CSV

    表  3  4种算法的运行时间

    方法总运行时常(ms)平均运行时间(ms)
    经典IMM1160.711.16
    文献[18]2391.523.92
    文献[19]1817.318.17
    本文算法(TIP-IMM)2513.825.14
    下载: 导出CSV
  • [1] PERKOVIČ M, GUCMA L, and FEUERSTACK S. Maritime security and risk assessments[J]. Journal of Marine Science and Engineering, 2024, 12(6): 988. doi: 10.3390/jmse12060988.
    [2] PRASETYO K A, ANSORI A, and SUSETO B. Maritime defense strategy education as an effort of the indonesian government in maintaining maritime security[J]. International Journal of Asian Education, 2023, 4(1): 58–67. doi: 10.46966/ijae.v4i1.325.
    [3] YUE Wenrong, XU Feng, and YANG Juan. Tracking-by-detection algorithm for underwater target based on improved multi-kernel correlation filter[J]. Remote Sensing, 2024, 16(2): 323. doi: 10.3390/rs16020323.
    [4] JIA Shuyi, ZHANG Yun, and WANG Guohong. Highly maneuvering target tracking using multi-parameter fusion singer model[J]. Journal of Systems Engineering and Electronics, 2017, 28(5): 841–850. doi: 10.21629/JSEE.2017.05.03.
    [5] ZHANG Licheng, PENG Kun, ZHAO Xingmo, et al. New fuel consumption model considering vehicular speed, acceleration, and jerk[J]. Journal of Intelligent Transportation Systems, 2023, 27(2): 174–186. doi: 10.1080/15472450.2021.2000406.
    [6] DENG Mingjun, LI Shuhang, JIANG Xueqing, et al. Vehicle trajectory prediction method based on “Current” statistical model and cubature Kalman filter[J]. Electronics, 2023, 12(11): 2464. doi: 10.3390/electronics12112464.
    [7] HAN Guoxing, LIU Fuyun, DENG Jucai, et al. An adaptive vehicle tracking enhancement algorithm based on fuzzy interacting multiple model robust cubature Kalman filtering[J]. Circuits, Systems, and Signal Processing, 2024, 43(1): 191–223. doi: 10.1007/s00034-023-02497-x.
    [8] BLOM H A P and BAR-SHALOM Y. The interacting multiple model algorithm for systems with markovian switching coefficients[J]. IEEE Transactions on Automatic Control, 1988, 33(8): 780–783. doi: 10.1109/9.1299.
    [9] SUN Lifan, ZHANG Jinjin, YU Haofang, et al. Tracking of maneuvering extended target using modified variable dtructure multiple-model based on adaptive grid best model augmentation[J]. Remote Sensing, 2022, 14(7): 1613. doi: 10.3390/rs14071613.
    [10] XU Hong, PAN Qin, XU Heng, et al. Adaptive IMM smoothing algorithms for jumping markov system with mismatched measurement noise covariance matrix[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(4): 5467–5480. doi: 10.1109/TAES.2024.3392552.
    [11] TIAN Ye, JIANG Hong, DING Quanxin, et al. Turn rate estimation based adaptive IMM algorithm for maneuvering target tracking[J]. Advanced Materials Research, 2012, 383/390: 5609–5614. doi: 10.4028/www.scientific.net/AMR.383-390.5609.
    [12] WANG Gang. ML estimation of transition probabilities in jump Markov systems via convex optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1492–1502. doi: 10.1109/TAES.2010.5545204.
    [13] LUO Yalun, LI Zhaoming, LIAO Yurong, et al. Adaptive Markov IMM based multiple fading factors strong tracking CKF for maneuvering hypersonic-target tracking[J]. Applied Sciences, 2022, 12(20): 10395. doi: 10.3390/app122010395.
    [14] 许登荣, 程水英, 包守亮. 自适应转移概率交互式多模型跟踪算法[J]. 电子学报, 2017, 45(9): 2113–2120. doi: 10.3969/j.issn.0372-2112.2017.09.009.

    XU Dengrong, CHENG Shuiying, and BAO Shouliang. Interacting multiple model algorithm based on adaptive transition probability[J]. Acta Electronica Sinica, 2017, 45(9): 2113–2120. doi: 10.3969/j.issn.0372-2112.2017.09.009.
    [15] LEE I H and PARK C G. An improved interacting multiple model algorithm with adaptive transition probability matrix based on the situation[J]. International Journal of Control, Automation and Systems, 2023, 21(10): 3299–3312. doi: 10.1007/s12555-022-0989-4.
    [16] ZHANG Zhao, GUO Hongwu, HE Jiaxing, et al. Adaptive interactive multiple model target tracking algorithm based on markov matrix with acceleration correction factor[C]. Proceedings of 2022 China Automation Congress (CAC), Xiamen, China, 2022: 3227–3232. doi: 10.1109/CAC57257.2022.10055933.
    [17] XIE Guo, SUN Lanlan, WEN Tao, et al. Adaptive transition probability matrix-based parallel IMM algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(5): 2980–2989. doi: 10.1109/TSMC.2019.2922305.
    [18] 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.
    [19] 王小敏, 雷筱, 张亚东. 基于改进自适应IMM算法的高速列车组合定位[J]. 电子与信息学报, 2024, 46(3): 817–825. doi: 10.11999/JEIT230251.

    WANG Xiaomin, LEI Xiao, and ZHANG Yadong. Combined positioning of high-speed train based on improved adaptive IMM algorithm[J]. Journal of Electronics & Information Technology, 2024, 46(3): 817–825. doi: 10.11999/JEIT230251.
    [20] LIBÓRIO M P, KARAGIANNIS R, DINIZ A M A, et al. The use of information entropy and expert opinion in maximizing the discriminating power of composite indicators[J]. Entropy, 2024, 26(2): 143. doi: 10.3390/e26020143.
    [21] LI Yuankai, LOU Jiaxin, TAN Xiaosu, et al. Adaptive kernel learning Kalman filtering with application to model-free maneuvering target tracking[J]. IEEE Access, 2022, 10: 78088–78101. doi: 10.1109/ACCESS.2022.3193101.
    [22] 孙大军, 张艺翱, 滕婷婷, 等. 单站水下方位频率机动目标运动分析方法[J]. 声学学报, 2024, 49(4): 683–695. doi: 10.12395/0371-0025.2023077.

    SUN Dajun, ZHANG Yiao, TENG Tingting, et al. A single-platform underwater maneuvering target motion analysis method based on bearing and frequency measurements[J]. Acta Acustica, 2024, 49(4): 683–695. doi: 10.12395/0371-0025.2023077.
    [23] TIAN Feng, ZHANG Haoyu, and FU Weibo. Research on extended target-tracking algorithms of sea surface navigation radar[J]. Electronics, 2023, 12(3): 616. doi: 10.3390/electronics12030616.
    [24] GUO Caifa, DAI Zhengxu, YANG Lei, et al. Application of the strong tracking UKF in the maneuvering target tracking[J]. Journal of Physics: Conference Series, 2016, 679(1): 012048. doi: 10.1088/1742-6596/679/1/012048.
    [25] DAI Hongde, DAI Shaowu, CONG Yuancai, et al. Performance comparison of EKF/UKF/CKF for the tracking of ballistic target[J]. TELKOMNIKA Indonesian Journal of Electrical Engineering, 2012, 10(7). doi: 10.11591/telkomnika.v10i7.1564. (查阅网上资料,未找到本条文献页码信息,请确认).
    [26] LIU Haoran, JIANG Qiumei, QIN Yuha, et al. Double layer weighted unscented Kalman underwater target tracking algorithm based on sound speed profile[J]. Ocean Engineering, 2022, 266: 112982. doi: 10.1016/j.oceaneng.2022.112982.
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出版历程
  • 收稿日期:  2025-01-20
  • 修回日期:  2025-06-30
  • 网络出版日期:  2025-07-04

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