The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model
At present, unmanned maritime equipment has become the main force in the implementation of marine exploration tasks. However, due to the complexity of the marine environment, equipment is susceptible to damage and loss. This is why achieving more effective search and rescue (SAR) of unmanned maritim...
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2262 |
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| author | Yuxuan Song Dezhi Wang Xiaodan Xiong Xinghua Cheng Lingzhi Huang Yichao Zhang |
| author_facet | Yuxuan Song Dezhi Wang Xiaodan Xiong Xinghua Cheng Lingzhi Huang Yichao Zhang |
| author_sort | Yuxuan Song |
| collection | DOAJ |
| description | At present, unmanned maritime equipment has become the main force in the implementation of marine exploration tasks. However, due to the complexity of the marine environment, equipment is susceptible to damage and loss. This is why achieving more effective search and rescue (SAR) of unmanned maritime equipment plays an extremely important role. The drifting trajectory and range predicted by the traditional methods are normally no longer corrected dynamically, which results in a low SAR efficiency. In this work, we propose a trajectory prediction and dynamic correction method based on a fully connected neural network (FCNN). It can dynamically correct the original predicted trajectory using the SAR target’s feedback of its own position information. This method can significantly improve the accuracy of SAR drifting trajectory and region prediction. In addition, the introduction of the dynamic correction model can also improve the adaptive capability and efficiency of the model. During the actual sea experiments, the average deviation distance between predicted and actual trajectories was reduced from 5.75 km to 4.11 × 10<sup>−1</sup> km by the proposed method. |
| format | Article |
| id | doaj-art-4ff9bc23f6c84c02b13f890d5c2a691e |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-4ff9bc23f6c84c02b13f890d5c2a691e2025-08-20T02:53:38ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212226210.3390/jmse12122262The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding ModelYuxuan Song0Dezhi Wang1Xiaodan Xiong2Xinghua Cheng3Lingzhi Huang4Yichao Zhang5College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaNo. 91001 Unit, PLA, Beijing 100036, ChinaNo. 92192 Unit, PLA, Ningbo 315000, ChinaAt present, unmanned maritime equipment has become the main force in the implementation of marine exploration tasks. However, due to the complexity of the marine environment, equipment is susceptible to damage and loss. This is why achieving more effective search and rescue (SAR) of unmanned maritime equipment plays an extremely important role. The drifting trajectory and range predicted by the traditional methods are normally no longer corrected dynamically, which results in a low SAR efficiency. In this work, we propose a trajectory prediction and dynamic correction method based on a fully connected neural network (FCNN). It can dynamically correct the original predicted trajectory using the SAR target’s feedback of its own position information. This method can significantly improve the accuracy of SAR drifting trajectory and region prediction. In addition, the introduction of the dynamic correction model can also improve the adaptive capability and efficiency of the model. During the actual sea experiments, the average deviation distance between predicted and actual trajectories was reduced from 5.75 km to 4.11 × 10<sup>−1</sup> km by the proposed method.https://www.mdpi.com/2077-1312/12/12/2262search and rescueunmanned maritime equipmentdrifting trajectory predictionsfully connected neural networkdynamic correction |
| spellingShingle | Yuxuan Song Dezhi Wang Xiaodan Xiong Xinghua Cheng Lingzhi Huang Yichao Zhang The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model Journal of Marine Science and Engineering search and rescue unmanned maritime equipment drifting trajectory predictions fully connected neural network dynamic correction |
| title | The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model |
| title_full | The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model |
| title_fullStr | The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model |
| title_full_unstemmed | The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model |
| title_short | The Prediction and Dynamic Correction of Drifting Trajectory for Unmanned Maritime Equipment Based on Fully Connected Neural Network (FCNN) Embedding Model |
| title_sort | prediction and dynamic correction of drifting trajectory for unmanned maritime equipment based on fully connected neural network fcnn embedding model |
| topic | search and rescue unmanned maritime equipment drifting trajectory predictions fully connected neural network dynamic correction |
| url | https://www.mdpi.com/2077-1312/12/12/2262 |
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