Radar Signal Recognition Method Based on Knowledge Distillation and Attention Map
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摘要: 针对传统雷达信号识别方法无法有效进行识别类型扩展问题，该文提出一种基于知识蒸馏与注意力图的雷达信号识别方法。首先将雷达信号的平滑伪Wigner-Ville分布(SPWVD)作为输入；然后设计了基于残差网络的增量学习网络结构，利用基于知识蒸馏与注意力图的损失函数，缓解类别增量过程中的灾难性遗忘；最后采用基于样本特征均值距离的方法对数据集进行管理，有效降低存储资源占用空间。实验表明，该方法能在存储资源有限的情况下，对扩展分类的信号快速完成训练，且对原有分类和扩展分类信号均有良好的识别准确率。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.
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表 2 扩展类型信号参数设置
信号类型 参数 变化范围 2FSK 载频f1,f2
码宽Tb0.10～0.45
(1/32～1/8)N4FSK 载频f1～f4
码宽Tb0.10～0.45
(1/32～1/8)NLFM-SFM 初始频率fc
最小频率fmin
最大频率fmax0.01～0.45
0.08～0.18
0.20～0.30MLFM 载频f0
带宽Δf0.01～0.25
0.10～0.45P1, P2 载频f0
步进频率M0.10～0.45
[4,5,6,7,8], P2取偶数P3, P4 载频f0
压缩比0.10～0.45
16～64
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