Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

Fei Teng, Yafei Song and Xinpeng Guo
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Fei Teng: Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Yafei Song: Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Xinpeng Guo: Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China

Mathematics, 2021, vol. 9, issue 19, 1-21

Abstract: The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent of information warfare. Moreover, if the traditional method of combat intention of air targets is based only on data from a single moment in time, the characteristic information on the time-series data is difficult to capture effectively. In this context, we design a new deep learning method attention mechanism with temporal convolutional network and bidirectional gated recurrent unit (Attention-TCN-BiGRU) to improve the recognition of the combat intent of air targets. Specifically, suitable characteristics are selected based on the combat mission and air posture to construct a characteristic set of air target intentions and encode them into temporal characteristics. Each characteristic in the characteristic set is given an appropriate weight through the attention mechanism. In addition, temporal convolutional network (TCN) is used to mine the data for latent characteristics and bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies in the data. Experiments comparing with other methods and ablation demonstrate that Attention-TCN-BiGRU outperforms state-of-the-art methods in terms of accuracy in recognizing target intent in the air.

Keywords: combat intention; bidirectional gated recurrent unit; attention mechanism; aerial target; temporal convolutional network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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