Abstract
The trajectory prediction of maritime targets is an urgent problem to be solved in order to effectively manage and control the situation in the sea area. The UAV has the advantages of strong real-time performance, high resolution and relatively stable viewing angle. Trajectory prediction of targets at sea. In this paper, a method of ship track prediction based on sliding window based bidirectional long short-term memory network (Bi-LSTM) is proposed using ship track data. Considering the high feature dimension of ship position information, the LSTM network theory is used to construct a ship track prediction model based on the sliding window BLSTM network, and the spatiotemporal features of the ship are extracted, and the position, speed and heading output of the network are used to predict the ship track. The simulation results show that, compared with the prediction results of LSTM and RNN, the prediction accuracy and generalization ability of the sliding window BLSTM network are better in the direct flight, steering and maneuvering scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xu, T., Cai, F., Hu, Q., et al.: Research on estimation of AIS vessel trajectory data based on Kalman filter algorithm. Mod. Electron. Tech. 37(5), 97–100, 104 (2014)
Perera, L.P., Oliveira, P., Guedes-Soares, C.: Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Trans. Intell. Transp. Syst. 13(3), 1188–1200 (2012)
Jee, G.I., Kim, H.S., Lee, Y.J., et al.: A GPS C/A code tracking loop based on extended Kalman filter with multipath mitigation. In: Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002), Portland, pp. 446–451 (2002)
Wang, Y., Li, H.: Research on forecast of the track of out-of-control ships in bridge waters. J. Wuhan Inst. Shipbuild. Technol. 10(4), 36–38 (2011)
Liu, X., Ruan, Q., Gong, Z.: New prediction model of ships GPS navigation positioning trajectory. J. Jiangnan Univ. (Nat. Sci. Ed.) 13(6), 686–692 (2014)
Lachlan, A.H.: A note on the elementary α-perceptron. Bull. Math. Biophys. 26(1), 45–47 (1964)
Tien, T.L.: A new grey prediction model FGM (1, 1). Math. Comput. Model. 49(7/8), 1416–1426 (2009)
Tong, X., Chen, X., Sang, L., et al.: Vessel trajectory prediction in curving channel of inland river. In: 2015 International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, pp. 25–28. IEEE, Piscataway (2015)
Xu, T., Liu, X., Yang, X.: BP neural network-based ship track real-time prediction. J. Dalian Marit. Univ. 38(1), 9–11 (2012)
Zhen, R., Jin, Y., Hu, Q., et al.: Vessel behavior prediction based on AIS data and BP neural network. Navig. China 40(2), 6–10 (2017)
Quan, B., Yang, B., Hu, K., et al.: Prediction model of ship trajectory based on LSTM. Comput. Sci. 45(z2), 126–131 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Wang, B., Su, Y., Wan, L., et al.: Sea-line detection method for surface unmanned vehicle based on gradient saliency. J. Opt. 36(5), 66–75 (2016)
Cai, J.X., Hu, J.L., Tang, X., et al.: Deep historical long short-term memory network for action recognition. Neurocomputing 407(3), 428–438 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Beijing HIWING Sci. and Tech. Info Inst
About this paper
Cite this paper
Hu, W., Zhang, W., Xiong, J. (2023). Ship Track Prediction Based on Sliding Window BLSTM Network. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_85
Download citation
DOI: https://doi.org/10.1007/978-981-99-0479-2_85
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0478-5
Online ISBN: 978-981-99-0479-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)