Research on Intent Recognition Method of Air Targets Based on Transformer Model

Most existing methods for operational intent recognition of air targets rely on complex models, leading to difficulties in accurately and efficiently identifying the true intent of the target. To overcome this challenge, this paper constructs an air target intent feature set by carefully selecting relevant features and encoding them into time-series data. Furthermore, a novel deep learning approach based on the Transformer model is proposed, aiming to enhance the capability of operational intent recognition for air targets. The Transformer model not only excels in extracting potential feature information from the data but also effectively captures the global relationships within the battlefield situation data information. Additionally, through its self-attention mechanism, Transformer adeptly handles long-term dependencies within sequences. Experimental results demonstrate that the Transformer model achieves an accuracy of 98.86% in air target intent recognition tasks, highlighting its superior performance in this domain.