Design on temporal-spatial Transformer model for air target intention recognition

Abstract

The battlefield changes rapidly in information warfare. The battlefield situation data presents massive and diversified characteristics, which makes it increasingly difficult to identify the operational intent of air targets based on expert experience. In combination with the current state-of-the-art intelligent methods, the Transformer model is studied and introduced into the field of air target intent recognition for the first time, and a new intent recognition method temporal-spatial transformer(TST) is designed, which can effectively mine the deep feature information in the temporal and spatial domains of battlefield situational data to improve the accuracy of air target combat intention recognition. In addition, a comparative study of the four currently advanced neural network intention recognition methods shows that TST achieved outstanding performance in all indicators and outperformed all compared neural network models. TST method not only has excellent accuracy but also extremely fast convergence rate, which allows it to quickly capture key information from battlefield situation data for intention recognition.

Keywords

• transformer
• temporal-spatial fusion
• air target
• inten recognition
• self-attention