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基于全球AIS的多源航迹关联数据集

崔亚奇 徐平亮 龚诚 余舟川 张建廷 于洪波 董凯

崔亚奇, 徐平亮, 龚诚, 余舟川, 张建廷, 于洪波, 董凯. 基于全球AIS的多源航迹关联数据集[J]. 电子与信息学报, 2023, 45(2): 746-756. doi: 10.11999/JEIT221202
引用本文: 崔亚奇, 徐平亮, 龚诚, 余舟川, 张建廷, 于洪波, 董凯. 基于全球AIS的多源航迹关联数据集[J]. 电子与信息学报, 2023, 45(2): 746-756. doi: 10.11999/JEIT221202
CUI Yaqi, XU Pingliang, GONG Cheng, YU Zhouchuan, ZHANG Jianting, YU Hongbo, DONG Kai. Multisource Track Association Dataset Based on the Global AIS[J]. Journal of Electronics & Information Technology, 2023, 45(2): 746-756. doi: 10.11999/JEIT221202
Citation: CUI Yaqi, XU Pingliang, GONG Cheng, YU Zhouchuan, ZHANG Jianting, YU Hongbo, DONG Kai. Multisource Track Association Dataset Based on the Global AIS[J]. Journal of Electronics & Information Technology, 2023, 45(2): 746-756. doi: 10.11999/JEIT221202

基于全球AIS的多源航迹关联数据集

doi: 10.11999/JEIT221202
基金项目: 国家自然科学基金 (61790554, 62001499, 62171453)
详细信息
    作者简介:

    崔亚奇:男,博士,副教授,研究方向为雷达数据处理、多源信息融合和人工智能交叉应用等

    徐平亮:男,博士生,研究方向为人工智能、信息融合等

    通讯作者:

    徐平亮 xu_pingliang@163.com

  • 中图分类号: TN957.52

Multisource Track Association Dataset Based on the Global AIS

Funds: The National Natural Science Foundation of China (61790554, 62001499, 62171453)
  • 摘要: 数据、算法和算力是当前人工智能技术发展的3大推力,考虑到智能关联算法研究的迫切需求和多雷达协同观测航迹数据获取困难,针对航迹关联数据集缺失问题,该文公开了多源航迹关联数据集(MTAD),其由全球AIS航迹数据经栅格划分、自动中断和噪声添加处理步骤构建。该数据集包括训练集和测试集两大部分,共有航迹百万余条,其中训练集包含5000个场景样本,测试集包含1000个场景样本,每一个场景样本由几个到几百个数量不等的航迹构成,涵盖多种运动模式、多种目标类型和长度不等的持续时间。同时,进一步对构造的MTAD数据集进行可视化分析,详细研究了各个栅格内航迹的特点,证明了该数据集的丰富性、合理性和有效性。最后,作为参考,给出了关联评价指标和关联基线结果。该数据集目前已被用作海军“金海豚”杯竞赛科目专用数据集。
  • 图  1  MMSI数量热力图

    图  2  MMSI数量分布柱状图

    图  3  关联样本生成流程图

    图  4  目标数量热力图

    图  5  目标数量分布柱状图

    图  6  目标密集程度热力图

    图  7  目标机动程度热力图

    图  8  目标机动程度分布柱状图

    图  9  典型场景展示

    表  1  生成信源航迹时所需的参数表

    参数数值
    Ts120 s
    Ts210 s
    W0(20°, 30°)
    Pd0.8
    Q10
    下载: 导出CSV

    表  2  多源关联基线结果(%)

    关联正确率关联错误率
    训练集80.8328.09
    测试集80.9828.15
    下载: 导出CSV

    表  3  中断关联基线结果(%)

    信源1信源2
    关联正确率关联错误率关联正确率关联错误率
    训练集28.1220.5028.2020.26
    测试集27.6219.4127.5019.63
    下载: 导出CSV
  • [1] BAR-SHALOM Y. Multitarget-Multisensor Tracking: Advanced Applications[M]. Norwood: Artech House, 1990.
    [2] ENDSLEY M R. Situation awareness global assessment technique (SAGAT)[C]. Proceedings of the IEEE 1988 National Aerospace and Electronics Conference, Dayton, USA, 1988: 789–795.
    [3] HALL D L and LLINAS J. An introduction to multisensor data fusion[J]. Proceedings of the IEEE, 1997, 85(1): 6–23. doi: 10.1109/5.554205
    [4] MUCCI R, ARNOLD J, and BAR-SHALOM Y. Track segment association with a distributed field of sensors[J]. The Journal of the Acoustical Society of America, 1985, 78(4): 1317–1324. doi: 10.1121/1.392901
    [5] YEOM S W, KIRUBARAJAN T, and BAR-SHALOM Y. Improving track continuity using track segment association[C]. 2003 IEEE Aerospace Conference Proceedings, Big Sky, USA, 2003, 4: 1925–1941.
    [6] LIN L, BAR-SHALOM Y, and KIRUBARAJAN T. New assignment-based data association for tracking move-stop-move targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(2): 714–725. doi: 10.1109/TAES.2004.1310016
    [7] ZHANG Shuo and BAR-SHALOM Y. Track segment association for GMTI tracks of evasive move-stop-move maneuvering targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 1899–1914. doi: 10.1109/TAES.2011.5937272
    [8] 齐林, 王海鹏, 熊伟, 等. 基于先验信息的多假设模型中断航迹关联算法[J]. 系统工程与电子技术, 2015, 37(4): 732–739. doi: 10.3969/j.issn.1001-506X.2015.04.02

    QI Lin, WANG Haipeng, XIONG Wei, et al. Track segment association algorithm based on multiple-hypothesis models with priori information[J]. Systems Engineering and Electronics, 2015, 37(4): 732–739. doi: 10.3969/j.issn.1001-506X.2015.04.02
    [9] XIONG Wei, XU Pingliang, CUI Yaqi, et al. Track segment association with dual contrast neural network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(1): 247–261. doi: 10.1109/TAES.2021.3098175
    [10] XIONG Wei, XU Pingliang, CUI Yaqi, et al. Track segment association via track graph representation learning[J]. IET Radar, Sonar & Navigation, 2021, 15(11): 1458–1471. doi: 10.1049/rsn2.12138
    [11] 徐平亮, 崔亚奇, 熊伟, 等. 生成式中断航迹接续关联方法[J]. 系统工程与电子技术, 2022, 44(5): 1543–1552. doi: 10.12305/j.issn.1001-506X.2022.05.15

    XU Pingliang, CUI Yaqi, XIONG Wei, et al. Generative track segment consecutive association method[J]. Systems Engineering and Electronics, 2022, 44(5): 1543–1552. doi: 10.12305/j.issn.1001-506X.2022.05.15
    [12] DENG Jia, DONG Wei, SOCHER R, et al. ImageNet: A large-scale hierarchical image database[C]. 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 248–255.
    [13] EVERINGHAM M, ESLAMI S M A, VAN GOOL L, et al. The PASCAL visual object classes challenge: A retrospective[J]. International Journal of Computer Vision, 2015, 111(1): 98–136. doi: 10.1007/s11263-014-0733-5
    [14] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context[C]. Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 740–755.
    [15] XIA Guisong, HU Jingwen, HU Fan, et al. AID: A benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7): 3965–3981. doi: 10.1109/TGRS.2017.2685945
    [16] LU Xiaoqiang, WANG Binqiang, ZHENG Xiangtao, et al. Exploring models and data for remote sensing image caption generation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(4): 2183–2195. doi: 10.1109/TGRS.2017.2776321
    [17] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: The KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231–1237. doi: 10.1177/0278364913491297
    [18] BILIC P, CHRIST P F, VORONTSOV E, et al. The liver tumor segmentation benchmark (LiTS)[J]. arXiv preprint arXiv: 1901.04056, 2019.
    [19] IRVIN J, RAJPURKAR P, KO M, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 590–597. doi: 10.1609/aaai.v33i01.3301590
    [20] DESAI S, BAGHAL A, WONGSURAWAT T, et al. Chest imaging representing a COVID-19 positive rural U. S. population[J]. Scientific Data, 2020, 7(1): 414. doi: 10.6084/m9.figshare.12980795
    [21] TETREAULT B J. Use of the automatic identification system (AIS) for maritime domain awareness (MDA)[C]. Proceedings of Oceans 2005 MTS/IEEE, Washington, USA, 2005: 1590–1594.
    [22] CHEN Zhijun, XUE Jie, WU Chaozhong, et al. Classification of vessel motion pattern in inland waterways based on automatic identification system[J]. Ocean Engineering, 2018, 161: 69–76. doi: 10.1016/j.oceaneng.2018.04.072
    [23] KONG Zhan, CUI Yaqi, XIONG Wei, et al. Ship target identification via Bayesian-transformer neural network[J]. Journal of Marine Science and Engineering, 2022, 10(5): 577. doi: 10.3390/jmse10050577
    [24] PAPI F, TARCHI D, VESPE M, et al. Radiolocation and tracking of automatic identification system signals for maritime situational awareness[J]. IET Radar, Sonar & Navigation, 2015, 9(5): 568–580. doi: 10.1049/iet-rsn.2014.0292
    [25] LIU Yong, YAO Libo, XIONG Wei, et al. GF-4 satellite and automatic identification system data fusion for ship tracking[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(2): 281–285. doi: 10.1109/LGRS.2018.2869561
    [26] SCHWEHR K D and MCGILLIVARY P A. Marine ship automatic identification system (AIS) for enhanced coastal security capabilities: An oil spill tracking application[C]. OCEANS 2007, Vancouver, Canada, 2007: 1–9.
    [27] CHEN M Y and WU H T. An automatic-identification-system-based vessel security system[J]. IEEE Transactions on Industrial Informatics, 2022, 19(1): 870–879.
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出版历程
  • 收稿日期:  2022-09-15
  • 修回日期:  2022-10-30
  • 网络出版日期:  2022-11-03
  • 刊出日期:  2023-02-07

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