Vessel trajectory prediction based on improved multi-output support vector

被引：0
|
作者：
Yang Z. [1 ]
Zhang Z. [1 ]
Shang X. [1 ]
Cao Z. [1 ]
Sun Z. [1 ]
机构：
[1] College of Intelligent Science and Engineering, Harbin Engineering University, Harbin
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 01期
关键词：
data driven; multi-output support vector regression (SVR); salp swarm algorithm (SSA); vessel trajectory prediction;
D O I：
10.12305/j.issn.1001-506X.2024.01.20
中图分类号：
学科分类号：
摘要：
In order to ensure the rapid, safe and reliable collision avoidance of intelligent vessel, this paper proposes a vessel track prediction model based on the improved salp swarm algorithm (SSA) multi output support vector is proposed. The multi output support vector model used in this paper can model the vessel as a whole, and the vessel model built can predict the changes of vessel track status at the same time. For the super parameters in the model, the improved SSA is used for optimization. The algorithm adds the characteristics of adaptive weight and outlier algorithm, avoiding the problem of premature algorithm and local optimization that is easy to get stuck in high-dimensional. Finally, the proposed method is validated by measured data and compared with other common models. The results show that the proposed method is feasible and effective. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：173 / 181
页数：8
相关论文
共 30 条
  • [11] LIN R Q, HUGHES M, SMITH T, Prediction of ship steering capabilities with a fully nonlinear ship motion model. Part 1: maneuvering in calm water[J], Journal of Marine Science and Technology, 15, 2, pp. 131-142, (2010)
  • [12] DASH A K, NAGARAJAN V, SHA O P, Bifurcation analysis of a high-speed twin-propeller twin-rudder ship maneuvering model in roll-coupling motion, Nonlinear Dynamics, 83, 4, pp. 2035-2053, (2016)
  • [13] LE Q I, ZHENG Z, Trajectory prediction of vessels based on data mining and machine learning[J], Journal of Digital Information Management, 14, 1, pp. 192-196, (2016)
  • [14] XU L, QIU H, Simplification and application of hydrodynamic model for ships sailing in still water, Journal of Anhui University of Technology: Natural Science Edition, 31, 2, pp. 162-166, (2014)
  • [15] ABRAHA M S, RAISEEM G, BANIAS L G, Et al., A robust and efficient stepwise regression method for building sparse polynomial chaos expansions, Journal of Computational Physics, 332, 1, pp. 461-474, (2016)
  • [16] ZHANG Y Y, WANG Z H, ZOU Z J, Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal, Ocean Engineering, 257, (2022)
  • [17] ZHUANG C X, CHEN C, Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm, Frontiers in Neurorobotics, 16, (2022)
  • [18] YE W Z., Design and implementation of ship trajectory prediction model based on machine learning and AIS data, (2022)
  • [19] HOU X R, ZOU Z J, LIU C, Nonparametric identification of nonlinear ship roll motion by using the motion response in irregular waves, Applied Ocean Research, 73, pp. 88-99, (2018)
  • [20] KAWAN B, HAO W, LI G, Et al., Data-driven modeling of ship motion prediction based on support vector regression, Proc. of the 58th Conference on Simulation and Modelling, (2017)
123