An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism

Traditional aerial target tactical intention recognition is based on a single moment of reasoning, while actual battlefield target tactical intention is realized by a series of actions, so the target state reflects dynamic and temporal variation. To solve this problem, bidirectional propagation and attention mechanisms are introduced based on a gated recurrent unit (GRU) network, and bidirectional gated recurrent units with attention mechanism (BiGRU-Attention) air target tactical intention recognition model is proposed. We use a hierarchical approach to construct the air combat intention characteristic set, encode it into temporal characteristics, encapsulate the decision-maker’s experience into labels, learn the deep-level information in the air combat intention characteristic vector through a BiGRU neural network, and use the attention mechanism to adaptively assign network weights, and then place air combat characteristic information with different weights in a softmax function layer for intention recognition. Comparison with a traditional air tactical target intention recognition model and analysis of ablation experiments show that the proposed model effectively improves the tactical intention recognition of air targets.