Data Augmentation for Radar Active Jamming Signals Based on Diffusion Models

Abstract

With the widespread application of deep learning in radar signal processing, the scarcity of radar signals has become one of the bottlenecks restricting the large-scale application of deep learning. To address this issue, this paper proposes a radar active jamming signal data augmentation method based on a diffusion model. By introducing the diffusion model, diverse radar jamming signals can be effectively generated, significantly enriching the training data. Experimental results demonstrate that the proposed method achieves notable performance improvements in signal enhancement and model training, providing strong support for the broad application of deep learning in the radar domain.

Keywords: Radar Data Augmentation, Deep learning, Diffusion Models