| 基于高斯原型网络的小样本逆合成孔径雷达目标识别 |
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| 引用本文： | 杨敏佳,白雪茹,刘士豪,曾磊,周峰.基于高斯原型网络的小样本逆合成孔径雷达目标识别[J].电子与信息学报,2022,44(10):3566-3573. |
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| 作者姓名： | 杨敏佳 白雪茹 刘士豪 曾磊 周峰 |
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| 作者单位： | 1.西安电子科技大学雷达信号处理国家重点实验室 西安 7100712.西安电子科技大学电子信息攻防对抗与仿真技术教育部重点实验室 西安 710071 |
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| 基金项目： | 国家自然科学基金(62131020, 61971332, 61631019) |
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| 摘 要： | 针对现有基于深度卷积神经网络(DCNNs)的逆合成孔径雷达(ISAR)目标识别方法在训练样本不足时性能下降甚至失效等问题，该文提出基于高斯原型网络(GPN)的小样本ISAR目标识别方法。该方法通过嵌入网络将ISAR像映射为嵌入向量，进而根据加权嵌入向量构建高斯原型，最终根据测试样本到原型的马氏距离预测目标类别。3类飞机目标实测数据的识别结果表明，该方法在小样本条件下可获得更高的平均识别精度。
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| 关 键 词： | 逆合成孔径雷达 目标识别 深度学习 小样本学习 高斯原型网络 |
| 收稿时间： | 2021-07-16 |
Small-Data Inverse Synthetic Aperture Radar Object Recognition Based on Gaussian Prototypical Network |
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| 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. |
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| Authors: | YANG Minjia BAI Xueru LIU Shihao ZENG Lei ZHOU Feng |
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| Affiliation: | 1.National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China2.Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi’an 710071, China |
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| 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. |
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