Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manually designed neural network models, find it difficult to maintain sustained and excellent performance in such a complex and changing environment. To address the problem of the adaptability of neural network models in complex environments, we propose a lightweight Transformer model (TransATIR) with a strong adaptive adjustment capability, based on the characteristics of air target intent recognition and the neural network architecture search technique. After conducting extensive experiments, it has been proved that TransATIR can efficiently extract the deep feature information from battlefield situation data by utilizing the neural architecture search algorithm, in order to quickly and accurately identify the real intention of the target. The experimental results indicate that TransATIR significantly improves recognition accuracy compared to the existing state-of-the-art methods, and also effectively reduces the computational complexity of the model.

Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making. Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models. Inspired by the success of diffusion models in addressing visual domain sample imbalances, this paper introduces a new approach that utilizes the Markov Transfer Field (MTF) method for time series data visualization. This visualization, when combined with the Denoising Diffusion Probabilistic Model (DDPM), effectively enhances sample data and mitigates noise within the original dataset. Additionally, a transformer-based model tailored for time series visualization and air target intent recognition is developed. Comprehensive experimental results, encompassing comparative, ablation, and denoising validations, reveal that the proposed method achieves a notable 98.86% accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities. This approach represents a promising avenue for advancing air target intent recognition.