基于多模态融合和深度学习的调制信号识别  

Modulation Signal Recognition Based on Multimodal Fusion and Deep Learning

作　　者：杨小蒙 张涛[1] 庄建军 乔晓强 杜奕航 YANG Xiaomeng;ZHANG Tao;ZHUANG Jianjun;QIAO Xiaoqiang;DU Yihang(The 63rd Research Institute,National University of Defense Technology,Nanjing 210007,China;School of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]国防科技大学第六十三研究所,南京210007 [2]南京信息工程大学电子与信息工程学院,南京210044

出　　处：《计算机科学》2023年第S02期693-699,共7页Computer Science

基　　金：国家自然科学基金项目(61801496,61801497);军委科技委基础加强计划领域基金项目(2019-JCJQ-JJ-221)。

摘　　要：针对现有的调制分类算法大多忽略了不同特征之间的互补性和特征融合的问题,提出了一种利用深度学习模型进行特征融合的方法。该方法试图融合调制信号的时序特征和空间特征,以获得差异性更加明显的识别特征。首先,获取调制信号的A/P信号和I/Q信号;然后,搭建卷积长短时记忆模块与复数密集残差卷积模块分别提取A/P信号的时序特征和I/Q信号的空间特征并将之进行融合,获取融合互补的识别特征;最后,将识别特征输入分类网络,得到识别结果。实验结果表明,基于开源数据集,当信噪比大于5 dB时,识别率达到了93.25%,与基于单一特征识别相比,识别准确率高出3%~11%;利用实际采集数据进行分类识别,进一步证实了所提特征提取模型与融合策略的有效性。Aiming at the problem that most of the existing modulation classification algorithms ignore the complementarity between different features and feature fusion,this paper proposes a method of feature fusion using deep learning model.This method attempts to fuse the temporal and spatial features of modulated signals to obtain more distinct recognition features.First,the A/P signal and I/Q signal of the modulation signal are obtained.Then,the convolution long-term and short-term memory module and the complex dense residual convolution module are built to extract the temporal features of A/P signal and the spatial features of I/Q signal respectively,and fuse them to obtain the fusion complementary recognition features.Finally,the recognition features are input into the classification network to obtain the recognition results.Experimental results show that based on the open source data set,when the signal-to-noise ratio is greater than 5 dB,the recognition rate reaches 93.25%,and the recognition accuracy is 3%~11%higher than that based on single feature recognition.The actual collected data is used for classification and recognition,which further proves the effectiveness of the proposed feature extraction model and fusion strategy.

关 键 词：自动调制分类 多模态融合 深度学习 

分 类 号：TN911.7[电子电信—通信与信息系统]