摘要
针对在复杂电磁空间中,通信信号识别方法存在噪声干扰、识别率不足、网络模型复杂等问题,提出基于高阶累积量可视化时频图像的FACAM-Efficient Net信号调制方式识别方法,通过高阶累积量时频分析方法,将不同调制的通信信号转换成可视化时频图像,用作网络模型的训练和测试,根据可视化时频图像的图像特征,提出FACAM频域通道混合注意力机制,并引入FACAM改进Efficient Net的网络主体结构,更好地提取时频图像的频域和通道的特征。实验结果显示,本文提出的调制识别方法在低信噪比下有较好的识别效果。
In view of the problems such as noise interference,insufficient recognition rate,and complex network model in communication signal recognition methods in complex electromagnetic space,the FACAM-Efficient Net signal modulation recognition method based on high-order cumulant visualization time-frequency images is proposed.Through high-order cumulant time-frequency analysis method,the communication signals of different modulations are converted into visualization time-frequency images for the training and testing of network models.According to the image features of visualization time-frequency images,the FACAM frequency-domain channel hybrid attention mechanism is proposed,and FACAM is introduced to improve the network main structure of Efficient Net so as to better extract the features of frequency-domain and channel of time-frequency images.Experimental results show that the modulation recognition method proposed in this paper has a good recognition effect at low signal-to-noise ratio.
作者
刘诚健
郑航
李智
Liu Cheng-jian;Zheng Hang;Li Zhi(College of Electronics and Information Engineering,Sichuan University,Chengdu 610000,Sichuan Province,China)
出处
《科学与信息化》
2024年第7期43-45,49,共4页
Technology and Information
关键词
高阶累积量
时频分析
调制识别
改进
high-order cumulant
time-frequency analysis
modulation recognition
improved Efficient Net