中国科学院空天信息创新研究院;中国科学院大学电子电气与通信工程学院;
随着通信环境的日益复杂化，信号调制识别变得越来越重要。针对低信噪比下数字信号调制识别困难的问题，提出一种基于小波阈值去噪与时频图像检测的调制识别方法。该方法将接收到的实信号转换成解析信号，通过小波阈值法对解析信号进行去噪处理。引入时频重排技术将去噪后的一维信号转换成二维时频图像，通过双线性插值缩放图像，得到适应网络输入大小的时频图。将时频图输入VGG网络中训练识别。实验结果显示，提出的调制识别方法对于低信噪比下的调制识别问题表现优异。
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[1] 张辉，曹丽娜.现代通信原理与技术[M].西安：西安电子科技大学出版社，2002.
[2] KULPA K,SZCZEPANKIEWICZ M,■YWEK M,et al.Cognitive Systems in Electronic Warfare[C]//XI Confe-rence on Reconnaissance & Electronic WARFARE Systems.[S.l.]:SPIE,2017:1041802.
[3] KIM K,POLYDOROS A.Digital Modulation Classification:the BPSK Versus QPSK Case[C]//MILCOM 88,21st Century Military Communications-What’s Possible?.Conference Record.Military Communications Conference.San Diego:IEEE,1988:431-436.
[4] POLYDOROS A,KIM K.On the Detection and Classification of Quadrature Digital Modulations in Broad-band Noise[J].IEEE Transactions on Communications,1990,38(8):1199-1211.
[5] LAY N E,POLYDOROS A.Modulation Classification of Signals in Unknown ISI Environments[C]//Proceedings of MILCOM 95.San Diego:IEEE,1995:170-174.
[6] ABDI A,DOBRE O A,CHOUDHRY R,et al.Modulation Classification in Fading Channels Using Antenna Arrays[C]//IEEE MILCOM 2004.Monterey:IEEE,2004:211-217.
[7] DOBRE O A,ABDI A,BAR-NESS Y,et al.Survey of Automatic Modulation Classification Techniques:Classical Approaches and New Trends[J].IET Communications,2007,1(2):137-156.
[8] SHERMEH A E Z,GHADERI R.An Intelligent System for Classification of the Communication Formats Using PSO[J].Informatica,2008,32(2):213-218.
[9] 徐哲，胡世安，吴钦，等.一种基于差分星座图的调制体制识别算法[J].计算机仿真，2009,26(11):182-185.
[10] 刘丹，马秀荣，单云龙.一种基于ST-RFT算法的数字调制信号识别方法[J].计算机科学，2018,45(5):64-68.
[11] 李秀梅，杨国青.几种时频分析方法的性能比较[J].计算机仿真，2015,32(3):220-224.
[12] ZHANG Q W,XU Z,ZHANG P Y.Modulation Scheme Recognition Using Convolutional Neural Network[J].The Journal of Engineering,2019,2019(23):9075-9078.
[13] DALDAL N,C?MERT Z,POLAT K.Automatic Determination of Digital Modulation Types with Different Noises Using Convolutional Neural Network Based on Time-frequency Information[J].Applied Soft Computing,2020,86(C):105834.
[14] 李红光，郭英，眭萍，等.基于时频特征的卷积神经网络跳频调制识别[J].浙江大学学报(工学版),2020,54(10):1945-1954.
[15] CHEN K Y,ZHANG S N,ZHU L Z,et al.Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning[J].Sensors,2021,21(2):449.
[16] DONOHO D L.De-noising by Soft-thresholding[J].IEEE Transactions on Information Theory,2002,41(3):613-627.
[17] AUGER F,FLANDRIN P,LIN Y T,et al.Time-frequency Reassignment and Synchrosqueezing:An Overview[J].IEEE Signal Processing Magazine,2013,30(6):32-41.
[18] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-scale Image Recognition[EB/OL].(2014-09-04) [2023-05-16].https://arxiv.org/abs/1409.1556.
[19] KAUR T,GANDHI T K.Automated Brain Image Classification Based on VGG-16 and Transfer Learning[C]//2019 International Conference on Information Technology (ICIT).Bhubaneswar:IEEE,2019:94-98.
[20] PAYMODE A S,MALODE V B.Transfer Learning for Multi-crop Leaf Disease Image Classification Using Con-volutional Neural Network VGG[J].Artificial Intelligence in Agriculture,2022,6(2):23-33.
[21] AI J J,HUA L J,LIU J,et al.Radar Signal Separation Re-cognition Method Based on Semantic Segmentation[C]//2021 8th International Conference on Dependable Systems and Their Applications(DSA).Yinchuan:IEEE,2021:302-306.
基本信息:
DOI：
中图分类号:TN911.3
引用信息:
[1]孙思燕,张伟雄,唐娉等.基于小波阈值去噪与时频图像检测的信号调制识别技术[J].无线电工程,2024,54(01):78-88.
基金信息:
国家自然科学基金(42192584)~~