Improved residual neural network algorithm for radar intra-pulse modulation classification

被引：0

作者：

Xu Z.-J. ^{[1
]}

Yang W.-T. ^{[2
]}

Yang C.-Z. ^{[3
]}

Tian Y.-T. ^{[2
,4
]}

Wang X.-J. ^{[1
]}

机构：

[1] School of Aeronautic Science and Engineering, Beihang University, Beijing

[2] College of Communication Engineering, Jilin University, Changchun

[3] College of Air Force Logistics, Aviation University of Air Force, Changchun

[4] Key Laboratory of Engineering Bionics, Ministry of Education, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2021年 / 51卷 / 04期

关键词：

Intra-pulse modulation classification; Pattern recognition and intelligent system; Radar signal; Residual neural network;

D O I：

10.13229/j.cnki.jdxbgxb20200447

中图分类号：

学科分类号：

摘要：

The artificially extracted features are computationally intensive and subjective, fail to fully reflect the nature of the signal, and take too long to generate time-frequency images. To overcome these problems, We propose an improved residual neural network (ResNet) ResNet32 as a framework to extract and identify radar time-domain signal features. We build a time-domain signal dataset of 9 types of intra-pulse signals and input them into the ResNet32 framework for training and classification. The algorithm saves a lot of time to generate time-frequency images, and the experimental verification algorithm has a better recognition rate at low signal to noise ratio (SNR). In the experimental conditions of mixed SNR, the recognition rate of the 9 modulation types with SNR=-14 dB and SNR=-8 dB achieved more than 90%. © 2021 Editorial Board of Journal of Jilin University (Engineering and Technology Edition). All right reserved.

引用

页码：1454 / 1460

页数：6

共 15 条

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