基于生成对抗数据增强支持向量机的小样本信号调制识别算法

着眼于解决小样本信号调制识别问题，该文首先研究了利用支持向量机(SVM)进行分类识别的理论可行性；其次根据统计学习理论，对利用生成对抗网络(GAN)生成数据增强支持向量机分类识别能力进行了理论分析；最后通过构建包含层归一化的深度卷积生成对抗网络(LDCGAN)，与普通深度卷积生成对抗网络相比，其生成数据映射至高维空间后特征更加明显，更有利于支持向量机的分类，实验验证了该生成对抗网络生成数据可以在小样本条件下实现对支持向量机分类识别能力的有效增强。

Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data

Focusing on solving the problem of small sample signal modulation recognition, the theoretical feasibility of using Support Vector Machine (SVM) for modulation recognition is investigated firstly; Secondly, based on statistical learning theory, a theoretical analysis of using Generative Adversarial Networks (GAN) generated data to enhance the classification ability of SVM is conducted; And finally, a Deep Convolutional Generative Adversarial Network based on Layer normalization (LDCGAN) is constructed , whose generated data has more obvious features than Deep Convolutional Generative Adversarial Networks (DCGAN) after mapping to a high-dimensional space, so the generated data is more conducive to the classification of SVM. The experiments verify that LDGAN generated data can achieve an effective enhancement of the classification ability of SVM under the condition of small samples.