Radar emitter identification based on unintentional phase modulation on pulse characteristic

被引：0
作者：
Qin X. [1 ]
Huang J. [1 ]
Wang J. [1 ]
Chen S. [1 ]
机构：
[1] School of Data and Target Engineering, Information Engineering University, Zhengzhou
来源：
Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Bezier curve; Deep learning; Long short term memory fully convolutional network; Radar emitter identification; Unintentional phase modulation on pulse;
D O I：
10.11959/j.issn.1000-436x.2020084
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：104 / 111
页数：7
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