Suppression of Radar Compound Interrupted Sampling and Repeating Jamming Based on Self-Supervised Learning Method

Abstract

By rapidly intercepting, storing, modulating, and forwarding a short segment of radar pulse signal, compound active deception main-lobe jamming could create preceding and lagging false targets that exhibit both deception and suppression effects with pulse compression gain, which significantly impairs the radar's ability to detect and track actual targets. This article proposes a self-supervised deep-learning-based method to automatically locate, recognize, and filter active deception jamming components. First, multiple compound active jamming types that could cooperatively and efficiently exhibit both preceding and lagging false target suppression effects against true target energy distribution are thoroughly analyzed. Then, to achieve the maximum separability between active jamming and true targets, the short-time Fourier transform is leveraged to fully reveal and depict the modulation texture and energy distribution of different jamming types that with various modulation modes and parameters in the 2-D time–frequency (TF) spectrum domain. To mitigate the dependence on prior jamming modes and parameter information, the label-free self-supervised feature extraction network that automatically detects and filters the multiple active jamming components in the 2-D TF spectrum domain is proposed. Both the measured and simulated jamming-contaminated echo data test results validate the effectiveness and robustness of the proposed method.