Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map

QU Zhiyu; LI Gen; DENG Zhian

doi:10.11999/JEIT210695

Volume 44 Issue 9

Sep. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3170-3177

QU Zhiyu, LI Gen, DENG Zhian. Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3170-3177. doi: 10.11999/JEIT210695

Citation:

QU Zhiyu, LI Gen, DENG Zhian. Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3170-3177. doi: 10.11999/JEIT210695

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Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map

doi: 10.11999/JEIT210695

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (61801143, 61971155)

Received Date: 2021-07-12
Accepted Date: 2022-01-25
Rev Recd Date: 2022-01-18

Available Online: 2022-02-19

Publish Date: 2022-09-19

Abstract

Abstract

In order to solve the problem that traditional radar signal recognition methods can not effectively expand the recognition types, a radar signal recognition method based on knowledge distillation and attention map is proposed. Firstly, the Smooth Pseudo Wigner-Ville Distribution (SPWVD) of the radar signal is used as input; Then, the incremental learning network structure based on residual network is designed, and the loss function based on knowledge distillation and attention map is used to alleviate the catastrophic forgetting in the process of category increment; Finally, a method based on the mean distance of sample features is used to manage the data set, which reduces effectively the occupied storage resources. Experiments show that this method can quickly complete the training of the extended classification signal under the condition of limited storage resources, and has good recognition accuracy for the original classification and the extended classification signal.
- Radar signal recognition,
- Incremental learning,
- Knowledge distillation,
- Attention map,
- Catastrophic forgetting

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References(20)

References

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