Attention based trajectory prediction method under the air combat environment

An Zhang, Baichuan Zhang, Wenhao Bi, Zeming Mao

科研成果: 期刊稿件文章同行评审

15 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)17341-17355
页数15
期刊Applied Intelligence
52
15
DOI
出版状态已出版 - 12月 2022

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