基于RE-GAN的调制信号开集识别算法  

Open-set recognition algorithm for modulation signal based on RE-GAN

作　　者：秦博伟 蒋磊[1] 许华[1] 牛伟宇 QIN Bowei;JIANG Lei;XU Hua;NIU Weiyu(Information and Navigation College,Air Force Engineering University,Xi’an 710077,China)

机构地区：[1]空军工程大学信息与导航学院,陕西西安710077

出　　处：《系统工程与电子技术》2023年第10期3321-3328,共8页Systems Engineering and Electronics

摘　　要：为解决轻量化模型下调制信号开集识别准确率不高的问题,设计了一种数据重建和极值理论生成对抗网络模型,包含一对相互对抗的网络:重建网络和判别网络。首先,重建网络利用自编码器对信号进行压缩重建,将高维度数据压缩为低维表达。然后,判别网络对压缩后的数据进行特征提取并建立各类数据的激活矢量集用以拟合极值分布。最后,通过极值理论求出已知和未知调制方式信号的概率。仿真实验表明,该模型显著降低了算法复杂度,不仅能对已知调制方式的信号进行充分学习和表达,还能干扰未知调制方式的信号,在信噪比大于0 dB时,针对8种已知调制方式和2种未知调制方式信号的识别准确率均达到93%。In order to solve the problem of open-set low recognition accuracy of modulation signal under the lightweight model,a model of data reconstruction and extremum theory generative adversarial network is designed.This model includes a pair of adversarial networks:reconstructed network and discriminant network.Firstly,the reconstructed network uses the autoencoder to compress and reconstruct the signal,and compresses the high-dimensional data into low-dimensional representation.Then,the discriminant network extracts the features of the compressed data and establishes the activation vector set of all kinds of data to fit the extremum distribution.Finally,the probability of known and unknown modulation signals is calculated by extremum theory.The experiment results show that the proposed model can not only fully learn and express the known modulation signals,but also disturb the unknown modulation signals.When the signal to noise ratio is greater than 0 dB,the recognition accuracy of eight known modulation signals and two unknown modulation signals can reach 93%.

关 键 词：数据重建 极值理论 生成对抗网络 开集识别 激活矢量 

分 类 号：TN975[电子电信—信号与信息处理]