Research on Ship Trajectory under Multi source Spatiotemporal Data
DOI:
https://doi.org/10.54691/cmqccp24Keywords:
Multi source data fusion, Ship trajectory analysis,Kalman filter, Bayesian estimation.Abstract
To explore the application of multi-source spatiotemporal data fusion technology in ship trajectory analysis, this study develops a fusion algorithm that integrates Kalman filtering and Bayesian estimation. Through the fusion of multi-source data, the positional accuracy of the trajectories is significantly improved, with experimental results showing that the average error is reduced from 58 meters to 23 meters. The results demonstrate that the proposed algorithm effectively enhances the accuracy and reliability of vessel monitoring, which is of great significance for maritime traffic safety.
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[1] Hui Du, Qian Chen: Analysis and Research on Satellite Navigation Application Standards,[J].Global Positioning System,2020, 45(3): 115-118.
[2] Haile Wu: Accuracy analysis of network RTK based on grid based high-precision satellite navigation positioning service method[J].Global Positioning System, 2022, 47(2): 99-103.
[3] Zhaojin Yan, Hui Yang, Hui Ci:Construction of Ship Trajectory Extraction Model and Traffic Flow Analysis,[J].Journal of Earth Information Science, 2023, 25(11): 2134-2149.
[4] Zhenping Xu: Research on Trajectory Analysis of Marine Transport Ships[J]. China water transport, 2017, 17(2): 16-17.
[5] Weilin Zhang: Trajectory mining method and framework construction based on ship data [D]. Tianjin: Tianjin University of Technology,2022
[6] Jia Li: Research on Ship Activity Analysis Method Based on AIS Data[D].Zhengzhou: Strategic Support Force Information Engineering University,2022
[7] Hai Zhou, Yaojie Chen, Li Chen: Cluster Analysis and Application of Ship Trajectory, [J]. Computer simulation, 2020, 37(10): 113-118,199.
[8] ROBARDS M D,SILBER G K,ADAMS J D,et al: Conservation Science and Policy Applications of the Marine Vessel Automatic Identification System(AIS)-a Review[J].Bulletin Marine Science, 2016, 92(01): 75-103.
[9] TANG H,YIN Y,SHEN :H·A model for vessel trajectory prediction based on long short-term memory neural network[J]. Journal of Marine Engineering & Technology,2022,21(03):136-145.
[10] ALIZADEHD,ALESHEIKH A A,SHARIF M.Vessel trajectory prediction using historical automatic identification system data[J].The Journal of Navigation,2021, 74(01):156-174.
[11] ZHOU P,SHI W,TIAN J,et al.Attention Based Bidirectional Long Short Term Memory Networks for Relation Classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics,2016:207-212.
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