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Intention recognition of aerial target based on deep learning

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Abstract

Three air target intention recognition methods based on deep learning are proposed to realize the function of recognizing air target intention based on real-time situation information to resolve the problem that pilots cannot effectively observe and analyze observation within a short period of time in complex air battlefield environments and traditional air target intention recognition algorithms have shortcomings such as complex feature filtering and reliance on expert experience. The methods use expert experience to simulate combat in the air on the simulation platform we designed, obtain and filter key posture information of aerial targets and corresponding expert script intention labels during combat, and sends them to a designed full connect network, a convolutional neural network and a recurrent neural network for training. The training results show that all three networks can achieve air target intention recognition, and the recurrent neural network based model can achieve the intention recognition with an accuracy of 80%. Compared with traditional methods, such as D-S inference, the proposed method is more general and robust. Finally, the feasibility and effectiveness of the method we proposed is verified by the simulation and experiments.

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Authors and Affiliations

  1. The 52nd Research Institute of China Electronics Technology Group Corporation, No.9, Wenfu Road, Yuhang, Hangzhou, 311100, China

    Chongxiao Qu, Zichang Guo, Shaojie Xia & Liaoyuan Zhu

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  1. Chongxiao Qu
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  2. Zichang Guo
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  4. Liaoyuan Zhu
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Correspondence to Zichang Guo.

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Qu, C., Guo, Z., Xia, S. et al. Intention recognition of aerial target based on deep learning. Evol. Intel. 17, 303–311 (2024). https://doi.org/10.1007/s12065-022-00728-9

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  • DOI: https://doi.org/10.1007/s12065-022-00728-9

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