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

被引：0
作者：
He L. [1 ,2 ]
Yang P. [1 ]
Yan X. [1 ,2 ]
Zhong X. [1 ]
Bai T. [1 ]
机构：
[1] School of Aeronautics and Astronautics, University of Electronic and Technology of China, Chengdu
[2] Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2024年 / 53卷 / 02期
关键词：
blind recognition; composite modulation; compressed sensing; frequency-domain data; lightweight neural network; unified carrier scheme;
D O I：
10.12178/1001-0548.2023096
中图分类号：
学科分类号：
摘要：
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. © 2024 University of Electronic Science and Technology of China. All rights reserved.
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页码：201 / 209
页数：8
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