Small-Data Inverse Synthetic Aperture Radar Object Recognition Based on Gaussian Prototypical Network

YANG Minjia; BAI Xueru; LIU Shihao; ZENG Lei; ZHOU Feng

doi:10.11999/JEIT210724

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3566-3573

YANG Minjia, BAI Xueru, LIU Shihao, ZENG Lei, ZHOU Feng. Small-Data Inverse Synthetic Aperture Radar Object Recognition Based on Gaussian Prototypical Network[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3566-3573. doi: 10.11999/JEIT210724

Citation:

YANG Minjia, BAI Xueru, LIU Shihao, ZENG Lei, ZHOU Feng. Small-Data Inverse Synthetic Aperture Radar Object Recognition Based on Gaussian Prototypical Network[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3566-3573. doi: 10.11999/JEIT210724

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Small-Data Inverse Synthetic Aperture Radar Object Recognition Based on Gaussian Prototypical Network

doi: 10.11999/JEIT210724

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62131020, 61971332, 61631019)

Received Date: 2021-07-16
Accepted Date: 2021-11-18
Rev Recd Date: 2021-11-15

Available Online: 2021-11-25

Publish Date: 2022-10-19

Abstract

Abstract

Considering the issue of performance degradation or even failure of the available Inverse Synthetic Aperture Radar (ISAR) object recognition methods based on Deep Convolution Neural Networks (DCNNs) with insufficient training samples, a small- data ISAR object recognition method based on Gaussian Prototypical Network (GPN) is proposed. Firstly, ISAR images are maped into embedding vectors by the embedding network, and then Gaussian prototypes are constructed according to the weighted embedding vectors. Finally, the object category is output according to the Mahalanobis distance from the test samples to all prototypes. Recognition results of the three different types of aircraft show that the proposed method can obtain higher average recognition accuracy under small-data scenarios.
- Inverse Synthetic Aperture Radar(ISAR),
- Object recognition,
- Deep learning,
- Small-data learning,
- Gaussian Prototypical Network(GPN)

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References(21)

References

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