High-Confidence Sample Augmentation Based on Label-Guided Denoising Diffusion Probabilistic Model for Active Deception Jamming Recognition | IEEE Journals & Magazine | IEEE Xplore

High-Confidence Sample Augmentation Based on Label-Guided Denoising Diffusion Probabilistic Model for Active Deception Jamming Recognition


Abstract:

Accurate recognition of the types of mainlobe active deception jamming is essential for radar systems to take antijamming countermeasures. A bunch of deep-learning (DL)-b...Show More

Abstract:

Accurate recognition of the types of mainlobe active deception jamming is essential for radar systems to take antijamming countermeasures. A bunch of deep-learning (DL)-based recognition methods that require large-scale datasets for training have shown promising results. However, capturing a sufficient number of diverse deception jamming samples is particularly intricate in actual dynamic and complex battlefields, the yielding limited or unbalanced datasets presents a significant challenge in training and generalizing DL models. This letter proposes a deep generative model, called the label-guided denoising diffusion probabilistic model (LG-DDPM), to address the issue of limited or class-imbalanced active deception jamming samples through data augmentation. By embedding label information into the diffusion process, the proposed model can generate and expand the active deception jamming samples specific to a predefined class even under low jamming-to-noise ratio (JNR) scenarios. The proposed method demonstrates superior performance in terms of both the fidelity and diversity of the generated jamming samples as well as the recognition accuracy of the DL recognizer when compared to state-of-the-art methods. Experimental results demonstrate the effectiveness and robustness of the proposed method.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 3508305
Date of Publication: 18 September 2023

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