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Attention based trajectory prediction method under the air combat environment

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Abstract

In close-range air combat, highly reliable trajectory prediction results can help pilots to win victory to a great extent. However, traditional trajectory prediction methods can only predict the precise location that the target aircraft may reach, which cannot meet the requirements of high-precision, real-time trajectory prediction for highly maneuvering targets. To this end, this paper proposes an attention-based convolution long sort-term memory (AttConvLSTM) network to calculate the arrival probability of each space in the reachable area of the target aircraft. More specifically, by segmenting the reachable area, the trajectory prediction problem is transformed into a classification problem for solution. Second, the AttConvLSTM network is proposed as an efficient feature extraction method, and combined with the multi-layer perceptron (MLP) to solve this classification problem. Third, a novel loss function is designed to accelerate the convergence of the proposed model. Finally, the flight trajectories generated by experienced pilots are used to evaluate the proposed method. The results indicate that the mean absolute error of the proposed method is no more than 45.73m, which is of higher accuracy compared to other state-of-the-art algorithms.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61903305 and 62073267), the Aeronautical Science Foundation of China (Grant No. 201905053001), the Research Funds for Interdisciplinary Subject, NWPU.

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  1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China

    An Zhang, Baichuan Zhang, Wenhao Bi & Zeming Mao

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  1. An Zhang
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Correspondence to Baichuan Zhang.

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Zhang, A., Zhang, B., Bi, W. et al. Attention based trajectory prediction method under the air combat environment. Appl Intell 52, 17341–17355 (2022). https://doi.org/10.1007/s10489-022-03292-y

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