AWSPNet: Attention-based Dual-Tree Wavelet Scattering Prototypical Network for MIMO Radar Target Recognition and Jamming Suppression

Abstract

The increasing of digital radio frequency memory based electronic countermeasures poses a significant threat to the survivability and effectiveness of radar systems. These jammers can generate a multitude of deceptive false targets, overwhelming the radar's processing capabilities and masking targets. Consequently, the ability to robustly discriminate between true targets and complex jamming signals, especially in low signal-to-noise ratio (SNR) environments, is of importance. This paper introduces the attention-based dual-tree wavelet scattering prototypical network (AWSPNet), a deep learning framework designed for simultaneous radar target recognition and jamming suppression. The core of AWSPNet is the encoder that leverages the dual-tree complex wavelet transform to extract features that are inherently robust to noise and signal translations. These features are further refined by an attention mechanism and a pre-trained backbone network. To address the challenge of limited labeled data and enhance generalization, we employ a supervised contrastive learning strategy during the training phase. The classification is performed by a prototypical network, which is particularly effective in few-shot learning scenarios, enabling rapid adaptation to new signal types. We demonstrate the efficacy of our approach through extensive experiments. The results show that AWSPNet achieves 90.45\% accuracy at -6 dB SNR. Furthermore, we provide a physical interpretation of the network's inner workings through t-SNE visualizations, which analyze the feature separability at different stages of the model. Finally, by integrating AWSPNet with a time-domain sliding window approach, we present a complete algorithm capable of not only identifying but also effectively suppressing various types of jamming, thereby validating its potential for practical application in complex electromagnetic environments.