Jamming Recognition Based on Feature Fusion and Convolutional Neural Network

被引：0
作者：
Liu S. [1 ]
Zhu C. [1 ]
机构：
[1] School of Information and Electronics, Beijing Institute of Technology, Beijing
来源：
Journal of Beijing Institute of Technology (English Edition) | 2022年 / 31卷 / 02期
关键词：
Convolutional neural network; Feature fusion; Jamming recognition; Power spectrum feature; Time-frequency image feature;
D O I：
10.15918/j.jbit1004-0579.2021.105
中图分类号：
学科分类号：
摘要：
The complicated electromagnetic environment of the BeiDou satellites introduces various types of external jamming to communication links, in which recognition of jamming signals with uncertainties is essential. In this work, the jamming recognition framework proposed consists of feature fusion and a convolutional neural network (CNN). Firstly, the recognition inputs are obtained by prepossessing procedure, in which the 1-D power spectrum and 2-D time-frequency image are accessed through the Welch algorithm and short-time Fourier transform (STFT), respectively. Then, the 1D-CNN and residual neural network (ResNet) are introduced to extract the deep features of the two prepossessing inputs, respectively. Finally, the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer. Results show the proposed method could reduce the impacts of potential feature loss, therefore improving the generalization ability on dealing with uncertainties. © 2022 Journal of Beijing Institute of Technology
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页码：169 / 177
页数：8
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