Ship Navigation Behavior Prediction Based on AIS Data
Real-time and accurate ship navigation dynamic prediction can effectively improve maritime supervision’s intelligence and precision level and ensure ship navigation safety. To further enhance the accuracy of ship navigation dynamic prediction, this paper uses ship AIS as the data source a...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9768818/ |
| _version_ | 1811309350102237184 |
|---|---|
| author | Tian Liu Jianwen Ma |
| author_facet | Tian Liu Jianwen Ma |
| author_sort | Tian Liu |
| collection | DOAJ |
| description | Real-time and accurate ship navigation dynamic prediction can effectively improve maritime supervision’s intelligence and precision level and ensure ship navigation safety. To further enhance the accuracy of ship navigation dynamic prediction, this paper uses ship AIS as the data source and proposes an ixmproved LSTM navigation dynamic prediction model based on the attention mechanism. Firstly, a set of pre-processing means, including navigation data extraction, abnormal data processing, and missing data interpolation, is proposed to solve the problems of information loss and inaccuracy in AIS data and incomplete retention of dynamic navigation features; secondly, combining the dynamic characteristics of ship navigation in AIS data with time series, using longitude, latitude, heading, speed, a ship heading and time increment as input to establish a dynamic forecasting model for navigation based on LSTM; The existing navigation sequence coding distortion and space-time data incoherence problem, an optimized Attention-LSTM neural network navigation dynamic prediction method is proposed, and the accuracy and robustness of the model are verified by simulation analysis. The results show that this method can achieve high-precision prediction of the ship’s longitude, latitude, heading, and speed. |
| first_indexed | 2024-04-13T09:40:42Z |
| format | Article |
| id | doaj.art-1d7291db6aea4f38bcb926dec4ec05da |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:40:42Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1d7291db6aea4f38bcb926dec4ec05da2022-12-22T02:51:56ZengIEEEIEEE Access2169-35362022-01-0110479974800810.1109/ACCESS.2022.31723089768818Ship Navigation Behavior Prediction Based on AIS DataTian Liu0https://orcid.org/0000-0001-8293-3864Jianwen Ma1https://orcid.org/0000-0003-3470-2636International Business School, Shandong Jiaotong University, Weihai, ChinaInternational Business School, Shandong Jiaotong University, Weihai, ChinaReal-time and accurate ship navigation dynamic prediction can effectively improve maritime supervision’s intelligence and precision level and ensure ship navigation safety. To further enhance the accuracy of ship navigation dynamic prediction, this paper uses ship AIS as the data source and proposes an ixmproved LSTM navigation dynamic prediction model based on the attention mechanism. Firstly, a set of pre-processing means, including navigation data extraction, abnormal data processing, and missing data interpolation, is proposed to solve the problems of information loss and inaccuracy in AIS data and incomplete retention of dynamic navigation features; secondly, combining the dynamic characteristics of ship navigation in AIS data with time series, using longitude, latitude, heading, speed, a ship heading and time increment as input to establish a dynamic forecasting model for navigation based on LSTM; The existing navigation sequence coding distortion and space-time data incoherence problem, an optimized Attention-LSTM neural network navigation dynamic prediction method is proposed, and the accuracy and robustness of the model are verified by simulation analysis. The results show that this method can achieve high-precision prediction of the ship’s longitude, latitude, heading, and speed.https://ieeexplore.ieee.org/document/9768818/AIS dataship navigationpredictionattention-LSTM |
| spellingShingle | Tian Liu Jianwen Ma Ship Navigation Behavior Prediction Based on AIS Data IEEE Access AIS data ship navigation prediction attention-LSTM |
| title | Ship Navigation Behavior Prediction Based on AIS Data |
| title_full | Ship Navigation Behavior Prediction Based on AIS Data |
| title_fullStr | Ship Navigation Behavior Prediction Based on AIS Data |
| title_full_unstemmed | Ship Navigation Behavior Prediction Based on AIS Data |
| title_short | Ship Navigation Behavior Prediction Based on AIS Data |
| title_sort | ship navigation behavior prediction based on ais data |
| topic | AIS data ship navigation prediction attention-LSTM |
| url | https://ieeexplore.ieee.org/document/9768818/ |
| work_keys_str_mv | AT tianliu shipnavigationbehaviorpredictionbasedonaisdata AT jianwenma shipnavigationbehaviorpredictionbasedonaisdata |