Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks
Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks
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DOI：
10.1109/lcomm.2018.2809732
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发表时间：
2018-05-01
期刊：
影响因子：
--
通讯作者：
Li, Shaoqian
中科院分区：
文献类型：
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作者：
Li, Rundong;Li, Lizhong;Li, Shaoqian
本文提出了一种基于抗噪处理和深度稀疏滤波卷积神经网络（AN-SF-CNN）的甚高频（VHF）无线电信号调制识别算法。首先计算调制信号的循环频谱，然后对循环频谱进行低秩表示，以减小VHF无线电信号中存在的干扰。然后，在对CNN进行微调之前，提出稀疏滤波准则对网络进行无监督的逐层预训练，有效地提高了网络的泛化能力。对7种调制信号进行了实验，仿真结果表明，与传统方法和一些著名的深度学习方法相比，该方法可以达到更高或相当的分类精度，并且对噪声具有鲁棒性。
This letter proposes a novel modulation recognition algorithm for very high frequency (VHF) radio signals, which is based on antinoise processing and deep sparse-filtering convolutional neural network (AN-SF-CNN). First, the cyclic spectra of modulated signals are calculated, and then, low-rank representation is performed on cyclic spectra to reduce disturbances existed in VHF radio signals. After that, before fine tuning the CNN, we propose a sparse-filtering criterion to unsupervised pretrain the network layer-by-layer, which improves generalization effectively. Several experiments are taken on seven kinds of modulated signals, and the simulation results show that, compared with the traditional methods and some renowned deep learning methods, the proposed method can achieve higher or equivalent classification accuracy, and presents robustness against noises.