Air target intent recognition method combining graphing time series and diffusion models

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.