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High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks
Wei Wang1, Chengwen Zhang1, Jinge Tian1
1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Computational Intelligence and Neuroscience
|August 1, 2020
Summary
New Inception-based VGG (IVGG) networks improve radar target recognition accuracy for high range resolution profile (HRRP) and synthetic aperture radar (SAR) signals. These networks offer simpler structures with fewer parameters, outperforming existing convolutional neural networks.
Area of Science:
- Radar systems engineering
- Artificial intelligence
- Machine learning
Background:
- Accurate radar target recognition is crucial for defense and surveillance.
- Existing convolutional neural networks (CNNs) face challenges in processing complex radar signals like HRRP and SAR.
- The Visual Geometry Group (VGG) network offers a robust architecture but can be computationally intensive.
Purpose of the Study:
- To develop novel CNNs, termed Inception-based VGG (IVGG) networks, for enhanced radar target recognition.
- To improve the efficiency and accuracy of target classification using HRRP and SAR data.
- To adapt and optimize deep learning architectures for the specific characteristics of radar signals.
Main Methods:
- Introduction of the Inception module into the VGG network architecture.
- Modification of the full connection layer's connection mode.
- Addition of a pointwise convolutional layer to enhance network nonlinearity.
- Comparative analysis against established CNNs (GoogLeNet, ResNet18, DenseNet121, VGG) on four distinct datasets.
Main Results:
- The proposed IVGG networks demonstrate superior classification accuracies compared to VGG and other benchmark CNNs.
- IVGG networks exhibit a simpler architecture with a reduced number of parameters.
- The integration of the Inception module effectively enhances feature extraction for radar targets.
- Improved non-linearity strengthens the network's ability to discern subtle target differences.
Conclusions:
- IVGG networks represent a significant advancement in high-resolution radar target recognition.
- The proposed architecture offers a more efficient and accurate solution for classifying HRRP and SAR signals.
- Further research can explore broader applications of IVGG networks in radar signal processing.
