Low complexity radar signal classification based on spectrum shape

Liang YIN; Rui LIN; Xiaolei WANG; Yuliang YAO; Lin ZHOU; Yuan HE

doi:10.11959/j.issn.1000-0801.2022011

您当前的位置：

首页 >

文章列表页 >

Low complexity radar signal classification based on spectrum shape

Research and Development | 更新时间：2024-06-05

- Low complexity radar signal classification based on spectrum shape
- Telecommunications Science Vol. 38, Issue 1, Pages: 25-35(2022)
- 作者机构：
  
  1. 北京邮电大学信息与通信工程学院，北京 100876
  2. 军事科学院国防科技创新研究院，北京 100071
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022011
  CLC： TN971.+1
- Published Online：2022-01，
  
  Published：20 January 2022
- 稿件说明：
移动端阅览
Liang YIN, Rui LIN, Xiaolei WANG, et al. Low complexity radar signal classification based on spectrum shape[J]. Telecommunications science, 2022, 38(1): 25-35.
DOI：

Liang YIN, Rui LIN, Xiaolei WANG, et al. Low complexity radar signal classification based on spectrum shape[J]. Telecommunications science, 2022, 38(1): 25-35. DOI： 10.11959/j.issn.1000-0801.2022011.

摘要

摘要：为解决雷达信号调制识别中存在的计算复杂度高、低信噪比环境识别准确率低和仿真数据真实度低等问题，提出了基于频谱形状的低复杂度雷达信号分类算法。对信号频谱进行归一化，按频谱采样的方法提取特征参数，训练机器学习分类模型。雷达信号源生成数据的测试结果表明，本算法对Barker码、Frank码、LFM、BPSK、QPSK 调制和常规雷达信号的分类准确率大于 90%（SNR≥3 dB），计算复杂度低，能适应信号参数变化，具有很好的泛化性。

Abstract

In order to solve the problems of high computational complexity

low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition

a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized

feature parameters were extracted by spectrum sampling method

and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code

Frank code

LFM code

BPSK

QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity

can adapt to the change of signal parameters

and has good generalization.

关键词

Keywords

references

HU H , WANG Y J , SONG J D . Signal classification based on spectral correlation analysis and SVM in cognitive radio [C ] // Proceedings of 22nd International Conference on Advanced Information Networking and Applications (AINA 2008) . Piscataway:IEEE Press , 2008 : 883 - 887 .

王渝冲 . 雷达信号调制类型识别与参数估计方法研究 [D ] . 长沙:国防科学技术大学 , 2012 .

WANG Y C . Research on the modulation recognition algorithms and parameter estimation algorithms of radar signal [D ] . Changsha:National University of Defense Technology , 2012 .

冯祥 , 李建东 . 调制识别算法及性能分析 [J ] . 电波科学学报 , 2005 , 20 ( 6 ): 737 - 740 .

FENG X , LI J D . Modulation classification algorithms and performance analysis [J ] . Chinese Journal of Radio Science , 2005 , 20 ( 6 ): 737 - 740 .

黄知涛 , 周一宇 , 姜文利 . 基于相对无模糊相位重构的自动脉内调制特性分析 [J ] . 通信学报 , 2003 , 24 ( 4 ): 153 - 160 .

HUANG Z T , ZHOU Y Y , JIANG W L . The automatic analysis of intrapulse modulation characte- ristics based on the relatively non-ambiguity phase restoral [J ] . Journal of China Institute of Communications , 2003 , 24 ( 4 ): 153 - 160 .

王丰华 , 黄知涛 , 姜文利 . 一种有效的脉内调制信号识别方法 [J ] . 信号处理 , 2007 , 23 ( 5 ): 686 - 689 .

WANG F H , HUANG Z T , JIANG W L . An effectual approach of intra-pulse modulated signal recognition [J ] . Signal Processing , 2007 , 23 ( 5 ): 686 - 689 .

刘鲁涛 , 戴亮军 , 陈涛 . 基于频谱复杂度的雷达信号调制方式识别 [J ] . 哈尔滨工程大学学报 , 2018 , 39 ( 6 ): 1081 - 1086 .

LIU L T , DAI L J , CHEN T . Radar signal modulation recognition based on spectrum complexity [J ] . Journal of Harbin Engineering University , 2018 , 39 ( 6 ): 1081 - 1086 .

QU Z Y , HOU C F , HOU C B , et al . Radar signal intra-pulse modulation recognition based on convolutional neural network and deep Q-learning network [J ] . IEEE Access , 2020 ( 8 ): 49125 - 49136 .

IGLESIAS V , GRAJAL J , ROYER P , et al . Real-time low-complexity automatic modulation classifier for pulsed radar signals [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2015 , 51 ( 1 ): 108 - 126 .

VALIEVA I , BJÖRKMAN M , ÅKERBERG J , et al . Multiple machine learning algorithms comparison for modulation type classification for efficient cognitive radio [C ] // Proceedings of MILCOM 2019 - 2019 IEEE Military Communications Conference . Piscataway:IEEE Press , 2019 : 318 - 323 .

LIN C , YAN W J , ZHANG L M , et al . A real-time modulation recognition system based on software-defined radio and multi-skip residual neural network [J ] . IEEE Access , 2020 ( 8 ): 221235 - 221245 .

KALEEM Z , ALI M , AHMAD I , et al . Artificial intelligence-driven real-time automatic modulation classification scheme for next-generation cellular networks [J ] . IEEE Access , 2021 ( 9 ): 155584 - 155597 .

LUNDEN J , KOIVUNEN V . Automatic radar waveform recognition [J ] . IEEE Journal of Selected Topics in Signal Processing , 2007 , 1 ( 1 ): 124 - 136 .

董志杰 , 王晓峰 , 田润澜 . 雷达信号脉内调制识别新方法 [J ] . 航天电子对抗 , 2018 , 34 ( 2 ): 33 - 37 .

DONG Z J , WANG X F , TIAN R L . A new method of radar signal intrapulse modulation recognition [J ] . Aerospace Electronic Warfare , 2018 , 34 ( 2 ): 33 - 37 .

孟凡杰 , 唐宏 , 王义哲 . 基于多特征融合的雷达辐射源信号识别 [J ] . 计算机仿真 , 2016 , 33 ( 3 ): 18 - 22 .

MENG F J , TANG H , WANG Y Z . Radar emitter signal recognition based on fusion of features [J ] . Computer Simulation , 2016 , 33 ( 3 ): 18 - 22 .

王渝冲 , 宿绍莹 , 陈曾平 . 基于小波变换的雷达信号调制类型识别方法 [J ] . 计算机工程 , 2012 , 38 ( 23 ): 166 - 168 , 172 .

WANG Y C , SU S Y , CHEN Z P . Method of radar signal modulation type recognition based on wavelet transform [J ] . Computer Engineering , 2012 , 38 ( 23 ): 166 - 168 , 172 .

胡杨林 . 跳频信号盲检测与参数盲估计算法研究及实现 [D ] . 成都:电子科技大学 , 2016 .

HUYANG L . Frequency hopping signal detection and parameter estimation algorithm and implementation [D ] . Chengdu:University of Electronic Science and Technology of China , 2016 .

Views

422

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A hybrid model for smart grid theft detection based on deep learning

Optimal active defense decision model of wireless communication network under APT attack

Research of 5G NR RedCap technology

Face recognition algorithm based on attractive local second gradient contours

A novel low-complexity two-tier group antenna selection algorithm

Related Author

Yaoyuan LIANG

Jun ZHANG

Yinling LIAO

Jincan LI

Bing WANG

Danni WANG

Meng MENG

Fuliang ZHANG

Related Institution

Guangxi Power Grid Co., Ltd.

Wuzhou Power Supply Bureau of Guangxi Power Grid Co., Ltd.

College of Electronics and Information Engineering, Shenzhen University

Institute of Applied Physics, Henan Academy of Sciences

Hangzhou Dianzi University

⁰