STABC-IR:An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

作     者：Siyuan WANG Gang WANG Qiang FU Yafei SONG Jiayi LIU Sheng HE Siyuan WANG;Gang WANG;Qiang FU;Yafei SONG;Jiayi LIU;Sheng HE

作者机构：Air Defense and Antimissile SchoolAir Force Engineering UniversityXi’an 710051China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2023年第36卷第3期

页      面：316-334页

核心收录：

学科分类：11[军事学] 0825[工学-航空宇航科学与技术] 1109[军事学-军事装备学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.62106283 and 72001214)。 

主　　题：Bidirectional gated recurrent network Conditional random field Intention recognition Intention transformation Situation cognition Space-time attention mechanism 

摘      要：The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.