Галерея 3176532

Галерея 3176532
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Proposed Few-Shot Learning for Radar Signal Recognition
Abstract: In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for... View more
In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for radar signal recognition, but deep learning-based algorithms only recognize trained classes. Recognizing novel radar signals with few-shot samples in an open environment is still a challenging research problem. In this letter, a few-shot learning algorithm based on the tensor imprint algorithm and convolutional classification layer is proposed for radar signal recognition, and the proposed convolutional classification layer can avoid spatial information loss caused by the global pooling layer and the fully connected layer. In addition, the lightweight re-parameterization multi-channel multi-branch convolutional neural network (RepMCMBNet) is proposed for feature extraction. The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9% at −6 dB when the number of samples is 5.
Published in: IEEE Signal Processing Letters ( Volume: 29 )
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In Recent years, with the rapid development of deep learning, the convolutional neural network has been widely used for radar signal recognition [1]–[4]. Researchers converted radar signals into time-frequency images through time-frequency analysis and used convolutional neural networks for automatic feature extraction and classification by supervised learning [4]–[9]. The radar signal recognition based on the convolutional neural network significantly improves the algorithm efficiency and recognition accuracy [10], [11]. However, in an open environment, the receiver will intercept novel radar signals. How to recognize novel radar signals with few samples in an open environment is still a challenge [12]–[17]. The subject of few-shot learning is to learn to recognize previously unseen classes with very few annotated examples.
2020 International Conference on UK-China Emerging Technologies (UCET)
2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP)
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

All Books Conferences Courses Journals & Magazines Standards Authors Citations
Proposed Few-Shot Learning for Radar Signal Recognition
Abstract: In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for... View more
In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for radar signal recognition, but deep learning-based algorithms only recognize trained classes. Recognizing novel radar signals with few-shot samples in an open environment is still a challenging research problem. In this letter, a few-shot learning algorithm based on the tensor imprint algorithm and convolutional classification layer is proposed for radar signal recognition, and the proposed convolutional classification layer can avoid spatial information loss caused by the global pooling layer and the fully connected layer. In addition, the lightweight re-parameterization multi-channel multi-branch convolutional neural network (RepMCMBNet) is proposed for feature extraction. The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9% at −6 dB when the number of samples is 5.
Published in: IEEE Signal Processing Letters ( Volume: 29 )
References is not available for this document.

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In Recent years, with the rapid development of deep learning, the convolutional neural network has been widely used for radar signal recognition [1]–[4]. Researchers converted radar signals into time-frequency images through time-frequency analysis and used convolutional neural networks for automatic feature extraction and classification by supervised learning [4]–[9]. The radar signal recognition based on the convolutional neural network significantly improves the algorithm efficiency and recognition accuracy [10], [11]. However, in an open environment, the receiver will intercept novel radar signals. How to recognize novel radar signals with few samples in an open environment is still a challenge [12]–[17]. The subject of few-shot learning is to learn to recognize previously unseen classes with very few annotated examples.
2020 International Conference on UK-China Emerging Technologies (UCET)
2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP)
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

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20. Mai 2022 The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9% at −6 dB when the number of samples is 5.
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20. Mai 2022 The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9% at −6 dB when the number of samples is 5.
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