Chinese Journal of Ship Research, Volume. 19, Issue 2, 223(2024)

Ship trajectory anomaly detection method based on encoder-decoder architecture composed of Transformer_LSTM modules

Kexin LI1, Jian GUO1, Ranchong LI2, Yujun WANG3, Zongming LI4, and Kun MIU5
Author Affiliations
  • 1Information Engineering University, Zhengzhou 450001, China
  • 2The 61221 Unit of PLA, Beijing 100000, China
  • 3The 32022 Unit of PLA, Guangzhou 510000, China
  • 4The 31682 Unit of PLA, Lanzhou 730000, China
  • 5Special Operations Command College, Guilin 541000, China
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    References(7)

    [4] PALLOTTA G, VESPE M, BRYAN K. Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction[J]. Entropy, 15, 2218-2245(2013).

    [13] [13] RISTIC B, LA SCALA B, MELE M, et al. Statistical analysis of motion patterns in AIS data: anomaly detection motion prediction[C]11th International Conference on Infmation Fusion. Cologne: IEEE, 2008: 1–7.

    [19] [19] YIN C Y, ZHANG S, WANG J, et al. Anomaly detection based on convolutional recurrent autoencoder f IoT time series[J]. IEEE Transactions on Systems, Man, Cyberics: Systems, 2022, 52(1): 112–122.

    [23] [23] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]Proceedings of the 31st International Conference on Neural Infmation Processing Systems. Long Beach: Curran Associates Inc. , 2017: 6000−6010.

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    Kexin LI, Jian GUO, Ranchong LI, Yujun WANG, Zongming LI, Kun MIU. Ship trajectory anomaly detection method based on encoder-decoder architecture composed of Transformer_LSTM modules[J]. Chinese Journal of Ship Research, 2024, 19(2): 223

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    Paper Information

    Category: Weapon, Electronic and Information System

    Received: Mar. 1, 2023

    Accepted: --

    Published Online: Mar. 18, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03291

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