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Intention recognition of UAV swarm with data-driven methods

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Abstract

UAVs have been increasingly used in military and commercial applications. The theory of UAV swarm behavir has gradually matured and moved to the real application stage. Fast and accurate recognition of the intentions of UAV swarms become a key part of dealing with coming swarms. This paper proposes a data-driven approach to realize the recognition of the typical intentions of UAV swarm. The UAV swarm’s intention is divided into three basic categories: expansion, free movement, and contraction. The dubins model is introduced to depict and study the dynamic characteristics of the movement of the UAV swarm. Simulation experiments are performed through software to collect data and to verify and refine the proposed data-driven intention recognition approach. Moreover, real flight experiments are conducted to test the feasibility and accuracy of the proposed approach, from which key steps about the neural network building and training for intention recognition have been summarized, and satisfying results in intention recognition with high accuracy and stability during the entire movement of the UAV swarm have been achieved.

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Acknowledgements

We would like to thank Dr. Xiaohui Zhang, Mr Zeyang Zhao and Mr Xiaoyun Sun from the School of Aeronautics and Astronautics at Shanghai Jiao Tong University for their technical support in our flight experiment.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62103275.

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Author notes

  1. Jiayun Chen and Jiaju Wang contributed equally to this work.

Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, No. 800 Dongchuan Rd., Minhang, 200240, Shanghai, China

    Zhichao Wang, Jiayun Chen, Jiaju Wang & Qiang Shen

Authors
  1. Zhichao Wang
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  2. Jiayun Chen
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  3. Jiaju Wang
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  4. Qiang Shen
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Correspondence to Qiang Shen.

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Cite this article

Wang, Z., Chen, J., Wang, J. et al. Intention recognition of UAV swarm with data-driven methods. AS 6, 703–714 (2023). https://doi.org/10.1007/s42401-023-00238-1

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  • DOI: https://doi.org/10.1007/s42401-023-00238-1

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