An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism
Fei Teng, Xinpeng Guo, Yafei Song, Wang Gang
Abstract
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.