An air target intention data extension and recognition model based on deep learning

• Published in: Scientific reports Vol. 15; no. 1; pp. 13894 - 24
• Main Authors: Cao, Bo, Xing, Qinghua, Li, Longyue, Lin, Weijie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/984; 639/705/117; Deep learning; Denoising diffusion probabilistic model; Diffusion models; Dilated causal convolution; Distillation; Graph attention network; Humanities and Social Sciences; Intention recognition; multidisciplinary; Science; Science (multidisciplinary); Situation cognition; Denoising diffusion probabilistic model; Intention recognition; Dilated causal convolution; Graph attention network; Situation cognition
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-98438-6

As the core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of data scarcity and insufficient temporal feature extraction in the process of air target intention recognition, an air target intention data extension and recognition model based on Deep Learning named IDERDL is proposed, where ID denotes intention data, E denotes data extension, R denotes intention recognition, and DL denotes deep learning. The model mainly consists of two parts: intention data generation and intention recognition. First, the air target intention recognition problem is analyzed in detail, and the air target intention space and feature set are constructed and coded uniformly. Second, the intention data generation model is built based on the denoising diffusion model, and the improved knowledge distillation method is applied to the denoising diffusion model to accelerate the sampling process of the model. Finally, the temporal block based on dilated causal convolution is built to solve the problem of temporal feature extraction. At the same time, the graph attention mechanism is introduced to mine and analyze the relationship between different features. The output of the graph attention mechanism will be channeled to the softmax layer for classification, which ultimately yields the result of target intention recognition at the current moment. The experimental results show that the intention recognition accuracy of the IDERDL model can reach 98.73%, which is better than most of the air target intention recognition methods. The IDERDL model considers the scarcity of intention data for the first time, as well as temporality, which is of great significance for improving tactical intention recognition capabilities.