Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to classify and recognize different targets in high range resolution profile (HRRP) and synthetic aperture radar (SAR) signals. The IVGG networks have been improved in two aspects. One is to adjust the connection mode of the full connection layer. The other is to introduce the Inception module into the visual geometry group (VGG) network to make the network structure more suik / for radar target recognition. After the Inception module, we also add a point convolutional layer to strengthen the nonlinearity of the network. Compared with the VGG network, IVGG networks are simpler and have fewer parameters. The experiments are compared with GoogLeNet, ResNet18, DenseNet121, and VGG on 4 datasets. The experimental results show that the IVGG networks have better accuracies than the existing convolutional neural networks.

中文翻译：

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通过基于初始的VGG（IVGG）网络进行的高分辨率雷达目标识别。

针对高分辨率雷达目标识别，提出了新的卷积神经网络，即基于初始的VGG（IVGG）网络，以对高分辨分辨率轮廓（HRRP）和合成孔径雷达（SAR）信号中的不同目标进行分类和识别。IVGG网络在两个方面进行了改进。一种是调整整个连接层的连接模式。另一种是将Inception模块引入视觉几何组（VGG）网络中，以使网络结构更易于识别/用于雷达目标识别。在Inception模块之后，我们还添加了一个点卷积层以增强网络的非线性。与VGG网络相比，IVGG网络更简单且参数更少。将实验与GoogLeNet，ResNet18，DenseNet121和VGG在4个数据集上进行了比较。