2022
DOI: 10.1049/rsn2.12286
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New classes inference, few‐shot learning and continual learning for radar signal recognition

Abstract: Automatic radar modulation recognition plays a significant role in both civilian and military applications. With the rapid development of deep learning, convolutional neural networks have achieved demonstrated success in radar signal recognition. However, the convolutional neural networks usually only recognise trained classes, and when the dataset changes, the networks need to be retrained. However, in actual radar signal recognition applications, the model usually needs to predict new radar signals, and the … Show more

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Cited by 9 publications
(2 citation statements)
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References 27 publications
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“…Meanwhile, in computer vision, the research on image retrieval (Triantafillou et al, 2017), object tracking (Bertinetto et al, 2016), radar signal recognition (Luo et al, 2022), and other image recognition (W. Wang et al, 2022;Zheng et al, 2023) based on small sample learning is developing rapidly. Xue et al (2023) proposed an adaptive cross-scenario few-shot learning framework for structural damage detection.…”
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confidence: 99%
“…Meanwhile, in computer vision, the research on image retrieval (Triantafillou et al, 2017), object tracking (Bertinetto et al, 2016), radar signal recognition (Luo et al, 2022), and other image recognition (W. Wang et al, 2022;Zheng et al, 2023) based on small sample learning is developing rapidly. Xue et al (2023) proposed an adaptive cross-scenario few-shot learning framework for structural damage detection.…”
mentioning
confidence: 99%
“…From the perspective of integrating knowledge-driven and data-driven approaches, Ref. [16,17] have defined the feature representation of radar signals in deep learning networks, and have achieved embedded knowledge through prior knowledge assistance in network training.…”
mentioning
confidence: 99%