An air target tactical intention recognition method based on the fusion deep learning network model

Pan Wang; Yongli Wang; Xiaoze Gong

doi:10.1117/12.2674845

2 May 2023 An air target tactical intention recognition method based on the fusion deep learning network model

Pan Wang, Yongli Wang, Xiaoze Gong

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422I (2023) https://doi.org/10.1117/12.2674845
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Aiming at the problem that the existing air target tactical intention recognition methods cannot use the sequential characteristic and the multiple dimension of sensor data effectively, an air target tactical intention recognition method based on the fusion deep learning network model is proposed. According to the specific combat task, we select appropriate target features and intentions to construct the feature space and intention space of the target. Then we obtain the real-time target state data as time feature sequences. We assign weights to different features through attention mechanism, use convolutional neural network (CNN) to obtain local trend features of the time series and shorten the time series, use time convolutional network (TCN) to extract short-term local features in the data, and use bidirectional gating recurrent unit (BiGRU) to extract long-term dependence on data and considerate the future information to achieve combat intent recognition of air targets. Through contrast experiments with other models, the effectiveness and advantages of the proposed method are demonstrated.

Citation Download Citation

Pan Wang, Yongli Wang, and Xiaoze Gong "An air target tactical intention recognition method based on the fusion deep learning network model", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422I (2 May 2023); https://doi.org/10.1117/12.2674845

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