赫彬 , 苏洪涛 . 认知雷达抗干扰中的博弈论分析综述 [J ] . 电子与信息学报 , 2021 , 43 ( 5 ): 1199 - 1211 .

HE B , SHU H T . A review of game theory analysis in cognitive radar anti-jamming [J ] . Journal of Electronics & Information Technology , 2021 , 43 ( 5 ): 1199 - 1211 . (in Chinese)

HUANG S , YAO Y Y , YAN X H , et al. Cumulant based maximum likelihood classification for overlapped signals [J ] . IEEE Electronics Letters , 2016 , 52 ( 21 ): 1761 - 1763 . DOI: 10.1049/ell2.v52.21 http://doi.org/10.1049/ell2.v52.21 https://onlinelibrary.wiley.com/toc/1350911x/52/21 https://onlinelibrary.wiley.com/toc/1350911x/52/21

SHI Q H , KARASAWA Y . Noncoherent maximum likelihood classification of quadrature amplitude modulation constellations: simplification,analysis,and extension [J ] . IEEE Transactions on Wireless Communications , 2011 , 10 ( 4 ): 1312 - 1322 . DOI: 10.1109/TWC.2011.030311.101490 http://doi.org/10.1109/TWC.2011.030311.101490 http://ieeexplore.ieee.org/document/5725207/ http://ieeexplore.ieee.org/document/5725207/

张笑宇 , 冯永新 , 钱博 . 一种分数域数字信号调制方式识别方法 [J ] . 弹箭与制导学报 , 2021 , 41 ( 1 ): 13 - 17 .

ZHANG X Y , FENG Y X , QIAN B . A modulation recognition method for digital signals in fractional domain [J ] . Journal of Projectiles,Rockets,Missiles and Guidance , 2021 , 41 ( 1 ): 13 - 17 . (in Chinese)

DONG S L , LI Z P , ZHAO L F . A Modulation recognition algorithm based on cyclic spectrum and SVM classification [C ] //Proceedings of the 2020 IEEE 4th Information Technology,Networking,Electronic and Automation Control Confe rence (ITNEC).Chongqing, China:IEEE , 2020 : 2123 - 2127 .

LIANG Y , XIANG X , SUN Y , et al. Novel modulation recognition for WFRFT-based system using 4th-order cumulants [J ] . IEEE Access , 2019 , 7 ( 1 ): 86018 - 86025 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

PENG S L , SUN S J , YAO Y D . A survey of modulation classification using deep learning: signal representation and data preprocessing [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 33 ( 2 ): 7020 - 7038 . DOI: 10.1109/TNNLS.2021.3085433 http://doi.org/10.1109/TNNLS.2021.3085433 https://ieeexplore.ieee.org/document/9454255/ https://ieeexplore.ieee.org/document/9454255/

LÜ H J , ZHOU X , HUO J H , et al. Joint OSNR monitoring and modulation format identification on signal amplitude histograms using convolutional neural network [J ] . IEEE Optical Fiber Technology , 2021 , 61 : 102455 .

张思成 , 林云 , 涂涯 , 等 . 基于轻量级深度神经网络的电磁信号调制识别技术 [J ] . 通信学报 , 2020 , 403 ( 11 ): 16 - 25 .

ZHANG S C , LIN Y , TU Y , et al. Electromagnetic signal modulation recognition technology based on lightweight deep neural network [J ] . Journal on Communications , 2020 , 403 ( 11 ): 16 - 25 . (in Chinese)

刘明骞 , 郑诗斐 , 李兵兵 . 基于深度学习的MPSK信号调制识别 [J ] . 国防科技大学学报 , 2019 , 41 ( 5 ): 153 - 158 .

LIU M Q , ZHENG S F , LI B B . Modulation recognition of MPSK signals based on deep learning [J ] . Journal of National University of Defense Technology , 2019 , 41 ( 5 ): 153 - 158 . (in Chinese)

O'SHEA T J , ROY T , CLANCY T C . Over the air deep learning based radio signal classification [J ] . IEEE Journal of Selected Topics in Signal Processing , 2017 ,2017, 99 : 1 - 1 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J ] . IEEE Advances in Neural Information Processing Systems , 2012 : 1097 - 1105 .

LIN R D , REN W J , SUN X , et al. A hybrid neural network for fast automatic modulation classification [J ] . IEEE Access , 2020 , 8 : 130314 - 130322 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

张天骐 , 范聪聪 , 葛宛营 , 等 . 基于ICA和特征提取的MIMO信号调制识别算法 [J ] . 电子与信息学报 , 2020 , 42 ( 9 ): 2208 - 2215 .

ZHANG T Q , FAN C C , GE W Y , et al. MIMO signal modulation recognition algorithm based on ICA and feature extraction [J ] . IEEE Journal of Electronics & Information Technology , 2020 , 42 ( 9 ): 2208 - 2215 . (in Chinese)

江伟华 , 童峰 , 王彬 , 等 . 采用主分量分析的非合作水声通信信号调制识别 [J ] . 兵工学报 , 2016 , 37 ( 9 ): 1670 - 1676 . DOI: 10.3969/j.issn.1000-1093.2016.09.017 http://doi.org/10.3969/j.issn.1000-1093.2016.09.017 由于信道传输特性、信噪比低等因素的影响，非合作水声通信信号的调制识别极具挑战性。对信号功率谱、平方谱进行主分量分析，提取代表不同类型调制信号特有信息的主分量作为特征参数，从而降低特征参数维度、抑制噪声影响，并在此基础上设计一种基于人工神经网络的水声通信信号调制方式分类器。海上实录信号数据的识别实验结果表明了该方法的有效性。

JIANG W H , TONG F , WANG B , et al. Modulation recogntion method of non-cooperation underwater acoustic communcation signals using principal component analysis [J ] . Acta Armamentarii , 2016 , 37 ( 9 ): 1670 - 1676 . (in Chinese)

安泽亮 , 张天骐 , 马宝泽 , 等 . 基于一维CNN的多入多出OSTBC信号协作调制识别 [J ] . 通信学报 , 2021 , 42 ( 7 ): 84 - 94 . DOI: 10.11959/j.issn.1000-436x.2021142 http://doi.org/10.11959/j.issn.1000-436x.2021142 为识别多入多出正交空时分组码（MIMO-OSTBC）系统所采用的调制样式，提出了一种基于一维卷积神经网络（1D-CNN）的协作调制识别算法。首先，采用迫零盲均衡来提升不同调制信号间区分度，并选用天然无损的同相正交（I/Q）信号作为浅层特征；然后，设计并训练基于 1D-CNN 的识别模型，从浅层特征中提取深层特征；最后，采用投票决策和置信度决策融合策略，提升多天线接收端协作识别精度。实验结果表明，所提算法能有效识别{BPSK,4PSK,8PSK,16QAM,4PAM}5种调制方式，当信噪比大于或等于-2 dB时，识别精度可达100%。

AN Z L , ZHANG T Q , MA B Z , et al. Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal [J ] . Journal on Communications , 2021 , 42 ( 7 ): 84 - 94 . (in Chinese) DOI: 10.11959/j.issn.1000-436x.2021142 http://doi.org/10.11959/j.issn.1000-436x.2021142 To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.

WEST N E , O'SHEA T J . Deep architectures for modulation recognition [C ] // Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). Baltimore,MD,US:IEEE , 2017 : 1 - 6 .

崔天舒 , 崔凯 , 黄永辉 , 等 . 卷积神经网络卫星信号自动调制识别算法 [J ] . 北京航空航天大学学报 , 2022 , 48 ( 6 ): 986 - 994 .

CUI T S , CUI K , HUANG Y H , et al. Convolutional neural network satellite signal automatic modulation recognition algorithm [J ] . Journal of Beijing University of Aeronautics and Astronautics , 2022 , 48 ( 6 ): 986 - 994 . (in Chinese)

SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition [EB/OL ] . IEEE Computer Science . http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.740.6937&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.740.6937&rep=rep1&type=pdf http://citeseerx.ist.psu.e du/viewdoc/download?doi=10.1.1.740.6937 & rep=rep1 & type=pdf,2014-11-18/2021-09-05.

SZEGEDY C , LIU W , JIA Y , et al. Going deeper with convolutions [EB/OL ] . IEEE Computer Society , https://ieeexplore.ieee.org/document/7298594 https://ieeexplore.ieee.org/document/7298594 https://ieeexplore.ieee.org/document/7298594,2015-07-12/2021-09-05.

HE K M , ZHANG X , REN S Q , et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J ] . IEEE Transactions on Pattern Analysis & Machine Intelligence , 2014 , 37 ( 9 ): 1904 - 1916 .