@inproceedings{cdbb2a49ce0447479782a5594b60e1ec,
title = "An End-to-End Intent Recognition Method for Combat Drone Swarm",
abstract = "In the field of intent recognition of combat drone swarm, traditional methods are based on the data characteristics which are only from a single target and at a single moment. It is difficult to capture the feature information of the entire swarm on time series. This paper proposes an end-to-end UAV swarm intent recognition method. Firstly, the distance threat coefficient and angle threat coefficient between UAVs are used to model the graph structure data of UAV swarm. Secondly, a novel deep learning method based on graph attention network, graph pooling method and gated recurrent unit (GAT-AP-GRU) is designed. This network can process the graph structure data obtained by modeling and identify the intention of the swarm. Experiments comparing with other methods and ablation experiments demonstrate that GAT-AP-GRU outperforms state-of-the-art methods in terms of accuracy of intent recognition.",
keywords = "Graph Network, Intent Recognition, Mapping Method",
author = "Hui He and Zhihong Peng and Peiqiao Shang and Wenjie Wang and Xiaoshuai Pei",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023 ; Conference date: 03-11-2023 Through 05-11-2023",
year = "2024",
doi = "10.1007/978-981-99-7590-7_14",
language = "English",
isbn = "9789819975891",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "167--177",
editor = "Bin Xin and Naoyuki Kubota and Kewei Chen and Fangyan Dong",
booktitle = "Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings",
address = "Germany",
}
