基于频域数据压缩感知的复合调制信号盲识别  

Blind Recognition for Composite Modulation Signal Based on Frequency-Domain Data Compressed Sensing

作　　者：何羚 阳鹏飞 阎啸 钟旭诺 白泰礼 HE Ling;YANG Pengfei;YAN Xiao;ZHONG Xunuo;BAI Taili(School of Aeronautics and Astronautics,University of Electronic and Technology of China,Chengdu 611731,China;Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province,Chengdu 611731,China)

机构地区：[1]电子科技大学航空航天学院,成都611731 [2]飞行器集群智能感知与协同控制四川省重点实验室,成都611731

出　　处：《电子科技大学学报》2024年第2期201-209,共9页Journal of University of Electronic Science and Technology of China

基　　金：四川省自然科学基金面上项目(2022NSFSC0545)。

摘　　要：现有基于特征提取和模式识别的复合调制盲识别方法对特征和样本量敏感,且在多子载波情况下流程繁琐。基于统一载波体制复合调制信号建模,提出了以压缩后的复合调制信号频域数据为特征、利用倒残差分组卷积结构的轻量化神经网络对压缩数据进行训练和分类的盲识别新思路。通过实验平台搭建和Python代码实现,对10种复合调制信号进行了不同信噪比下的盲识别对比试验,结果表明:该方法在0 dB信噪比条件下识别率可达94.5%,5 dB信噪比条件下识别率为100%;精确识别所需数据量少于基于统计特征和决策树的识别方法,分类所用神经网络的准确率和参数量亦优于基准网络。Modern TT&C(Tracking,Telemetry and Command)system mostly adopts the composite modulation in a form of“pulse coding/multi-subcarrier internal modulation/external modulation”.This complicated scheme brings great challenges to signal accurate recognition in the absence of prior information and low signal-to-noise ratio(SNR)scenario.The existing composite modulation blind recognition methods based on feature extraction and pattern recognition are sensitive to signal features and sample size,and the whole process becomes even more cumbersome in the case of multiple subcarriers.In this paper,based on the unified carrier system composite modulated signal modeling,a new idea of blind recognition is proposed to train and classify the compressed composite modulated signal frequency domain data by using the inverse residual packet convolutional structure of lightweight neural network.By means of experiment platform construction and Python code designing,the proposed method verification for 10 composite modulated signals in condition of various SNRs is implemented.The results show that the recognition accuracy of the proposed method can reach 94.5%(SNR=0 dB)and 100%(SNR=5 dB)respectively;moreover,the sample size required for equal recognition accuracy is less than the existing statistical features and decision tree-based methods,and both the performance and amount of neural networks parameters used for classification are better than those of the benchmark network.

关 键 词：复合调制 盲识别 统一载波体制 频域数据 压缩感知 轻量化神经网络 

分 类 号：TN971.1[电子电信—信号与信息处理]