Abstract
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.
Highlights
Radar automatic target recognition (RATR) technology can provide inherent characteristics of the target, such as the attributes, categories, and models, and these characteristics can provide richer information for battlefield command decisions. e high-resolution radar echo signal obtained from the wide bandwidth signal transmitted by the broadband radar provides more detailed features of the target, which makes it possible to identify the target type. erefore, more and more research studies focus on RATR technology
Dataset. e synthetic aperture radar (SAR) image dataset used in this paper is a public dataset released by MSTAR. ere are many research studies on radar automatic target recognition based on the MATAR SAR dataset, such as references [1–4, 17–20]. e experimental results in this paper are compared with the above methods. e MSTAR dataset and the high range resolution profile (HRRP) dataset are used for experiments
In the extended operating condition (EOC)-1 dataset, there are 4 kinds of ground targets, in which the targets with a side view angle of 17° are used for the training set and the targets with a side view angle of 30° are used for the test set
Summary
Radar automatic target recognition (RATR) technology can provide inherent characteristics of the target, such as the attributes, categories, and models, and these characteristics can provide richer information for battlefield command decisions. E high-resolution radar echo signal obtained from the wide bandwidth signal transmitted by the broadband radar provides more detailed features of the target, which makes it possible to identify the target type. Erefore, more and more research studies focus on RATR technology
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