A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors - PAN Ming; ZHENG Jingsong; LI Jinliang; FANG Long; YANG Yang; ZHAO Shijie (Journal of Harbin University of Science and Technology)
Aiming at the problems of multiple data sources, multiple data modes, high data dimensions, large redundancy, small and unbalanced sample size, and difficulty in obtaining a large number of labeled data required for training, a deep bidirectional gated recurrent unit (DBGRU) electromagnetic behavior intent recognition model is constructed. By integrating the attention mechanism in the Bidirectional Gated Recurrent Unit (BiGRU) , the feature learning ability of the model is improved, and adaptively assign the weight of different air combat feature information. With DBGRU as the backbone network, a few-shot contrastive learning algorithm based on data augmentation is proposed, which uses the Wasserstein Generative Adversarial Network (WGAN) based on Wasserstein distance to enrich the original data, and uses the contrastive learning framework to mine the rich pattern information in the multimodal data to make up for the lack of few-shot data, so as to accurately predict the behavior intention of electromagnetic targets. The experimental simulation results show that the accuracy of the few-shot contrastive learning algorithm based on data augmentation in predicting the behavior intention of few-shot air combat targets is 91. 13% .