Abstract
Fleets usually voyage in different formations for different missions, which means there exists a close tie between the fleet’s intention and its formation. In this paper, the fleet formation recognition problem is studied where ships’ trajectories are the only available information through detection. First, the commonly used fleet formations as well as the corresponding dataset generation method are introduced. Then, the recognition algorithm is designed based on Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network by constructing the relative position relationship between ships to a graph. Specifically, the network model designed in this paper can extract features from unordered trajectories. Finally, simulation experimental results are provided and analyzed, which show the effectiveness and generalization ability of the proposed algorithm.
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Lin, Z., Zhang, X., He, F. (2023). A GNN-LSTM-Based Fleet Formation Recognition Algorithm. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_702
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DOI: https://doi.org/10.1007/978-981-19-6613-2_702
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