Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network
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摘要: 为满足雷达舰船目标识别的高实时性和高泛化性的需求，该文提出了一种基于深度多尺度1维卷积神经网络的目标高分辨1维距离像(HRRP)识别方法。针对高分辨1维距离像特征提取难的问题，所提方法通过共享卷积核的权值，使用多尺度的卷积核提取不同精细度的特征，并构造中心损失函数来提高特征的分辨能力。实验结果表明，该模型可以显著提高目标在非理想条件下的识别正确率，克服目标姿态角敏感性问题，具有良好的鲁棒性和泛化性。Abstract: In order to meet the demand for high real-time and high generalization performance of radar recognition, a radar High Resolution Range Profile (HRRP) recognition method based on deep multi-scale one dimension convolutional neural network is proposed. The multi-scale convolutional layer that can represent the complex features of HRRP is designed based on two features of the convolution kernels which are weight sharing and extraction of different fineness features from different scales, respectively. At last, the center loss function is used to improve the separability of features. Experimental results show that the model can greatly improve the accuracy of the target recognition under non-ideal conditions and solve the problem of the target aspect sensitivity, which also has good robustness and generalization performance.
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表 1 所提模型中主要的特征提取层的参数个数
卷积层 多尺度卷积层1 多尺度下采样层1 多尺度卷积层2 多尺度下采样层2 多尺度卷积层3 全连接层 合计 24 1552 4480 6016 4480 5760 2560 24872 
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表 2 CNN中主要的特征提取层的参数个数
卷积层32, 5×1 卷积层32, 5×1 卷积层64, 3×1 卷积层64, 3×1 卷积层128, 1×1 卷积层128, 1×1 全连接层2 合计 160 5120 6144 12288 8192 16384 2048 50336
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表 4 7种舰船目标的结构参数(m)
舰船编号 舰长 舰宽 吃水深度 1 182.8 24.1 8.1 2 172.8 16.8 6.5 3 153.8 20.4 6.3 4 135.0 16.8 4.5 5 121.0 17.6 4.3 6 102.2 16.5 4.2 7 89.3 12.1 4.0
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表 6 所提模型与对比模型在不同信噪比条件下的目标识别正确率(%)
模型名称 SNR (dB) 5 10 15 本文模型 95.12 97.67 98.90 CNN+CL 93.88 95.89 97.56 SDAE+MLP 90.58 92.15 93.22 SAE+ELM 90.94 92.63 94.05
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