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

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Yizhen Jia , Siyao Xiao , Wenkai Jia , Hui Chen , Wen-Qin Wang · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2510.18422

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.

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preprint

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http://arxiv.org/abs/2510.18422

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https://arxiv.org/pdf/2510.18422

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green

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https://openalex.org/W4416056024

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https://openalex.org/W4416056024

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DOI

https://doi.org/10.48550/arxiv.2510.18422

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Title

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

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preprint

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2025

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2025-10-21

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Yizhen Jia, Siyao Xiao, Wenkai Jia, Hui Chen, Wen-Qin Wang

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https://arxiv.org/abs/2510.18422

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https://arxiv.org/pdf/2510.18422

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Yes

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green

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https://arxiv.org/pdf/2510.18422

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