Paper
23 February 2023 Short-term vessel trajectory prediction based on Bi-LSTM by using AIS data from the Hainan-1 satellite
Author Affiliations +
Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 1255106 (2023) https://doi.org/10.1117/12.2668279
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China
Abstract
Ship trajectory prediction with high accuracy plays a significant role in maritime traffic management. The collision can be effectively decreased with the help of real-time prediction to plan a navigation course and monitor the ship's travel status. We propose a short-term trajectory prediction method based on bidirectional long short-term memory (Bi-LSTM) network by using AIS data from the Hainan-1 satellite. The main steps include (1) eliminating the abnormal data by filtering the historical data, smoothing the trace by linear interpolation, and normalizing into uniformly distributed time-series data; (2) creating the Bi-LSTM model; (3) predicting the next position of the ship. The experimental results show that the model has a relatively low root-mean-square error, which demonstrates its efficiency for trajectory prediction and can be utilized to avoid collisions and improve the safety of maritime traffic.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wenjuan Qi, Zhiheng Liu, Hang Yu, Suiping Zhou, Wenjie Zhang, and Yuru Guo "Short-term vessel trajectory prediction based on Bi-LSTM by using AIS data from the Hainan-1 satellite", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 1255106 (23 February 2023); https://doi.org/10.1117/12.2668279
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KEYWORDS
Artificial intelligence

Data modeling

Satellites

Error analysis

Education and training

Interpolation

Tunable filters

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