High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks
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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2020-01-01
|
| Series: | Computational Intelligence and Neuroscience |
| Online Access: | http://dx.doi.org/10.1155/2020/8893419 |
| Summary: | 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. |
|---|---|
| ISSN: | 1687-5265 1687-5273 |