Dilated CNN Design Approach for Extracting Multi-Scale Features in Radar Emitter Classification

Abstract

Radar emitter classification plays an increasingly significant role in the electronic reconnaissance system. Due to many convolutional neural network (CNN)-based approaches suffer from insufficient spatial receptive fields and inadequate feature representation, the classification accuracy is poor in low signal-to-noise ratio (SNR) conditions. Therefore, in this paper, we stress the importance of multi-scale dilated convolutions for target feature extraction, and propose two novel CNN architecture design approaches called multi-scale dilated residual network (MDRN). By combining multi-scale dilated convolutions with residual architecture, MDRN not only has a larger receptive field, but also can learn more diverse features, thereby improving the ability to process time-frequency images (TFI) under high-noise energy conditions. Moreover, compared with the original residual model, MDRN does not increase any parameter complexity or floating-point operations per second (FLOPS). Experiments on the TFI classification task show that the proposed MDRN has superior performance over state-of-the-art CNN models.

Keywords

• Intra-pulse modulation classification
• dilated convolution
• feature maps
• multi-scale feature fusion
• time-frequency image