Abstract
Since a target's operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific nor effective enough. Based on a gated recurrent unit (GRU), a bidirectional propagation mechanism and attention mechanism are introduced in a proposed aerial target combat intention recognition method. The proposed method constructs an air combat intention characteristic set through a hierarchical approach, encodes into numeric time-series characteristics, and encapsulates domain expert knowledge and experience in labels. It uses a bidirectional gated recurrent units (BiGRU) network for deep learning of air combat characteristics and adaptively assigns characteristic weights using an attention mechanism to improve the accuracy of aerial target combat intention recognition. In order to further shorten the time for intention recognition and with a certain predictive effect, an air combat characteristic prediction module is introduced before intention recognition to establish the mapping relationship between predicted characteristics and combat intention types. Simulation experiments show that the proposed model can predict enemy aerial target combat intention one sampling point ahead of time based on 89.7% intent recognition accuracy, which has reference value and theoretical significance for assisting decision-making in real-time intention recognition.
Highlights
With the development of military and aviation technology, informationization has gradually become the focus of the modern battlefield
Aiming at the drawbacks of the above methods, Ou et al [13] proposed an intelligent recognition model of tactical intention based on a long shortterm memory (LSTM) network. e input characteristic of Computational Intelligence and Neuroscience the model is 12 consecutive frames of time sequence characteristics, which can effectively overcome the judgment by a single moment
Based on the above analysis, we propose a gated recurrent unit (GRU) based intelligent prediction model for aerial target combat intention. e model has characteristic prediction and intention recognition modules. e intention recognition module introduces a bidirectional propagation mechanism, attention mechanism, and particle swarm optimization (PSO) algorithm based on a GRU to build an intelligent intention recognition model
Summary
With the development of military and aviation technology, informationization has gradually become the focus of the modern battlefield. Us, the above methods are not scientific enough as they determine the enemy target operational intention using characteristic information at a single moment. Aiming at the drawbacks of the above methods, Ou et al [13] proposed an intelligent recognition model of tactical intention based on a long shortterm memory (LSTM) network. Based on the above analysis, we propose a gated recurrent unit (GRU) based intelligent prediction model for aerial target combat intention. Experiments show that the proposed model can predict the enemy aerial target operational intention one sampling point in advance, and the accuracy rate is increased by 2.9% compared with LSTM.
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