Radar emitter identification based on unintentional phase modulation on pulse characteristic

Xin QIN; Jie HUANG; Jiantao WANG; Shiwen CHEN

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

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Radar emitter identification based on unintentional phase modulation on pulse characteristic

Papers | 更新时间：2024-06-05

- Radar emitter identification based on unintentional phase modulation on pulse characteristic
- Journal on Communications Vol. 41, Issue 5, Pages: 104-111(2020)
- 作者机构：
  
  信息工程大学数据与目标工程学院，河南郑州 450001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61501513)
- DOI：10.11959/j.issn.1000-436x.2020084
  CLC： TN971.+1
- Online First：2020-05，
  
  Published：25 May 2020
- 稿件说明：
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Xin QIN, Jie HUANG, Jiantao WANG, et al. Radar emitter identification based on unintentional phase modulation on pulse characteristic[J]. Journal on Communications, 2020, 41(5): 104-111.
DOI：

Xin QIN, Jie HUANG, Jiantao WANG, et al. Radar emitter identification based on unintentional phase modulation on pulse characteristic[J]. Journal on Communications, 2020, 41(5): 104-111. DOI： 10.11959/j.issn.1000-436x.2020084.

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。

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.

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references

周志文 , 黄高明 , 陈海洋 , 等 . 雷达辐射源识别算法综述 [J ] . 电讯技术 , 2017 , 57 ( 8 ): 973 - 980 .

ZHOU Z W , HUANG G M , CHEN H Y , et al . An overview of radar emitter recognition algorithms [J ] . Telecommunication Engineering , 2017 , 57 ( 8 ): 973 - 980 .

刘博 . 辐射源个体识别技术的发展现状及应用建议 [J ] . 电子信息对抗技术 , 2019 , 34 ( 4 ): 40 - 43 .

LIU B . Development and application suggestion on technology of specific emitter identification [J ] . Electronic Information Warfare Technology , 2019 , 34 ( 4 ): 40 - 43 .

冷鹏飞 , 徐朝阳 . 一种深度强化学习的雷达辐射源个体识别方法 [J ] . 兵工学报 , 2018 , 39 ( 12 ): 2420 - 2426 .

LENG P F , XU C Y . Specific emitter identification based on deep reinforcement learning [J ] . Acta Armamentarii , 2018 , 39 ( 12 ):24202426.

邢小鹏 . 基于时空信息融合的雷达辐射源个体识别系统设计与实现 [D ] . 郑州:信息工程大学 , 2018 .

XING X P . Design and implementation of radar emitter individual identification system based on spatio-temporal information fusion [D ] . Zhengzhou:Information Engineering University , 2018 .

叶浩欢 , 柳征 , 姜文利 . 考虑多普勒效应的脉冲无意调制特征比较 [J ] . 电子与信息学报 , 2012 , 34 ( 11 ): 2654 - 2659 .

YE H H , LIU Z , JIANG W L . A comparison of unintentional modulation on pulse features with the consideration of Doppler effect [J ] . Journal of Electronics ＆ Information Technology , 2012 , 34 ( 11 ): 2654 - 2659 .

陈沛铂 , 李纲 . 应用动态时间规整算法实现雷达辐射源个体识别 [J ] . 信号处理 , 2015 ( 8 ): 1035 - 1040 .

CHEN P B , LI G . Applying dynamic time warping algorithm to specific radar emitter identification [J ] . Journal of Signal Processing , 2015 ( 8 ): 1035 - 1040 .

RU X H , LIU Z , JIANG W L . Recognition performance analysis of instantaneous phase and its transformed features for radar emitter identification [J ] . IET Radar,Sonar ＆ Navigation , 2015 , 10 ( 5 ): 945 - 952 .

RU X H , HUANG Z , LIU Z . Frequency-domain distribution and band-width of unintentional modulation on pulse [J ] . Electronics Letters , 2016 , 52 ( 22 ): 1853 - 1855 .

RU X H , LIU Z , HUANG Z T . Evaluation of unintentional modulation for pulse compression signals based on spectrum asymmetry [J ] . IET Radar,Sonar ＆ Navigation , 2017 , 11 ( 4 ): 656 - 663 .

LI L , JI H B , JIANG L . Quadratic time-frequency analysis and sequential recognition for specific emitter identification [J ] . IET Signal Processing , 2011 , 5 ( 6 ): 568 - 574 .

DUDCZYK J . A method of feature selection in the aspect of specific identification of radar signals [J ] . Bulletin of the Polish Academy of Sciences Technical Sciences , 2017 , 65 ( 1 ): 113 - 119 .

DIGNE F , BAUSSARD A , CORNU C . Classification of radar pulses in a naval warfare context using Bezier curve modeling of the instantaneous frequency law [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2017 , 53 ( 3 ): 1469 - 1480 .

KARIM F , MAJUMDAR S , DARABI H . LSTM fully convolutional networks for time series classification [J ] . IEEE Access , 2017 , 6 ( 99 ): 1662 - 1669 .

BAGNALL A , LINES J , BOSTROM A . The great time series classification bake off:a review and experimental evaluation of recent algorithmic advances [J ] . Data Mining and Knowledge Discovery , 2016 , 31 ( 3 ): 606 - 660 .

KARIM F , MAJUMDAR S , DARABI H . Insights into LSTM fully convolutional networks for time series classification [J ] . IEEE Access , 2019 : 1 - 7 .

HOCHREITER S , SCHMIDHUBER J . Long short-term memory [J ] . Neural Computation , 1997 , 9 ( 8 ): 1735 - 1780 .

LEA C , VIDAL R , REITER A , et al . Temporal convolutional networks:a unified approach to action segmentation [C ] // European Conference on Computer Vision . Berlin:Springer , 2016 : 47 - 54 .

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