Generative adversarial network based data augmentation and its application in few-shot electromagnetic signal classification

Abstract:

For few-shot electromagnetic signal classification, data augmentation is the most intuitive strategy. In this paper, Generative Adversarial Network(GAN) is employed to generate fake signal samples. The coarse-grained and fine-grained screening mechanisms are designed to screen the generated fake signals. The generated signals with poor quality are removed and the effective expansion of training dataset is realized. In order to verify the effectiveness of the proposed data augmentation algorithm, sufficient experiments are conducted on the RADIOML 2016.04C dataset. Experimental results show that the proposed method can improve the accuracy of few-shot electromagnetic signal classification effectively.