A Method of Recognizing Air Target Intent Based on Light Reverse Transformer
WANG Ke
GUO Xiangke
WANG Yanan
NI Peng
QUAN Wen
LI Chenghai
Abstract：Recognition of air target intent occupies a position of strategic importance in the realm of battle-field situational awareness.Nonetheless,how to quickly and accurately extract pertinent information from extensive situational data is still a question in this domain.The majority of prevalent research models are characterized by intricate architectures,hindering the efficient inference of target intentions within a con-cise timeframe.For the above-mentioned reasons,a model is introduced based on Transformer architec-ture.The model is optimized by Reverse method to adapt it further to handle time-series tasks.And,the integration of perturbation elements merged into the position encoding elevates the model's robustness and generalization capabilities.Additionally,this paper implements lightweight enhancements to both the at-tention mechanism and the feedforward neural network.By a comprehensive evaluation encompassing comparative experiments,ablation studies,and an in-depth analysis of computational complexity,the effi-cacy of the proposed model is unequivocally substantiated within the domain of airborne target intent reco-gnition.
Keywords：intent recognitiondeep learningTransformermulti-head attention
Publication Date：2025-06-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 96-105 )