基于无意调相特性的雷达辐射源个体识别

doi:10.11959/j.issn.1000-436x.2020084

通信学报 ›› 2020, Vol. 41 ›› Issue (5): 104-111.doi: 10.11959/j.issn.1000-436x.2020084

基于无意调相特性的雷达辐射源个体识别

秦鑫,黄洁,王建涛,陈世文

信息工程大学数据与目标工程学院，河南郑州 450001

修回日期:2020-03-16 出版日期:2020-05-25 发布日期:2020-05-30
作者简介:秦鑫（1994- ），女，重庆人，信息工程大学博士生，主要研究方向为电子侦察、机器学习|黄洁（1973- ），女，河南郑州人，信息工程大学教授、博士生导师，主要研究方向为信息融合、模式识别|王建涛（1984- ），男，河南商丘人，信息工程大学讲师，主要研究方向为雷达数据处理|陈世文（1974- ），男，湖南益阳人，信息工程大学教授、硕士生导师，主要研究方向为电子侦察
基金资助:
国家自然科学基金资助项目(61501513)

Radar emitter identification based on unintentional phase modulation on pulse characteristic

Xin QIN,Jie HUANG,Jiantao WANG,Shiwen CHEN

School of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China

Revised:2020-03-16 Online:2020-05-25 Published:2020-05-30
Supported by:
The National Natural Science Foundation of China(61501513)

摘要/Abstract

摘要：

针对脉内无意调相实现雷达辐射源个体识别时存在的分类模型性能不佳的问题，提出了一种长短时记忆加全卷积网络的雷达辐射源个体识别方法。首先给出了脉内信号相位的简化观测模型，并对观测相位序列进行去斜处理，提取无意调相的含噪估计；然后利用贝塞尔曲线拟合无意调相，降低噪声的影响，获得无意调相更为精确的描述；最后利用长短时记忆加全卷积网络提取无意调相序列的联合特征，实现雷达辐射源个体自动识别。仿真实验以及实测数据实验均验证了所提算法的可行性与有效性，实验结果表明，所提算法识别正确率高、耗时短。

关键词: 雷达辐射源个体识别, 无意调相, 贝塞尔曲线, 深度学习, 长短时记忆加全卷积网络

Abstract:

Aiming at the problem of poor performance of the classification model in the case of unintentional phase modulation on pulse (UPMOP) to achieve radar specific emitter identification,a method for radar specific emitter identification with long and short-term memory and full convolutional networks (LSTM-FCN) was proposed.Firstly,a simplified observation model of the intrapulse signal phase considering the intentional modulation was presented,and the observation phase sequence was deramp to extract the noisy estimate of the UPMOP.Then Bezier curve was utilized to fit the UPMOP to reduce the influence of noise and obtain a more accurate description of UPMOP.Finally,the LSTM-FCN was used to extract the joint features of UPMOP sequence to realize the radar specific emitter automatic identification.Both the simulation experiments and the measured data experiments verify the feasibility and effectiveness of the proposed algorithm.Moreover,the proposed algorithm has high identification accuracy and short time consumption.

Key words: radar emitter identification, unintentional phase modulation on pulse, Bezier curve, deep learning, long short term memory fully convolutional network

中图分类号:

TN971.+1

秦鑫,黄洁,王建涛,陈世文. 基于无意调相特性的雷达辐射源个体识别[J]. 通信学报, 2020, 41(5): 104-111.

Xin QIN,Jie HUANG,Jiantao WANG,Shiwen CHEN. Radar emitter identification based on unintentional phase modulation on pulse characteristic[J]. Journal on Communications, 2020, 41(5): 104-111.

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识别算法	平均识别耗时/s	平均识别正确率
KNN-ED	0.982 4	0.758 7
KNN-DTW	71.331 5	0.834 1
LSTM-FCN	1.484 3	0.885 7

基于无意调相特性的雷达辐射源个体识别

Radar emitter identification based on unintentional phase modulation on pulse characteristic

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