一种基于JANET模型的雷达信号分选方法

doi:10.7523/j.issn.2095-6134.2021.06.013

中国科学院大学学报 ›› 2021, Vol. 38 ›› Issue (6): 825-831.DOI: 10.7523/j.issn.2095-6134.2021.06.013

一种基于JANET模型的雷达信号分选方法

姜在阳^1,2,3, 孙思月², 李华旺², 梁广²

1. 中国科学院上海微系统与信息技术研究所, 上海 201250;
2. 中国科学院微小卫星创新研究院, 上海 201203;
3. 中国科学院大学, 北京 100049

收稿日期:2019-09-29 修回日期:2020-09-09 发布日期:2021-11-16
通讯作者: 孙思月
基金资助:
上海市启明星计划（18QA1404000）和中国科学院青年创新促进会资助

A method for deinterleaving based on JANET

JIANG Zaiyang^1,2,3, SUN Siyue², LI Huawang², LIANG Guang²

1. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;
2. Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-09-29 Revised:2020-09-09 Published:2021-11-16

摘要/Abstract

摘要： 随着电磁环境复杂度的不断提升及电子侦察体制的日益复杂，高脉冲丢失率下的复杂雷达脉冲信号分选成为制约电子侦察效能的瓶颈问题。针对实际应用中侦收脉冲数据丢失率高、到达时间抖动、信号体制复杂等问题，提出一种基于长短期记忆网络的变体JANET网络的雷达信号分选方法。该分选算法通过仅增加遗忘门结构，即可有效挖掘脉冲时间序列上下文的特性，实现高丢失率辐射源脉冲的有效分选，同时解决了循环神经网络长序列依赖问题，能够实现脉冲的准实时在线分选，满足工程应用中信号分选准确度及实时性的要求。

关键词: 雷达信号分选, JANET网络, 去交织, 脉冲流

Abstract: Radar signal deinterleaving process is a method of classifying intensive pulse streams. The performance of signal classifiers requires to be improved when being confronted with the large amount of data and mode-switch emitters. Recurrent neural network is appropriate as a classifier for pulse streams. However it is weak of long-term dependencies. The forget gate which is a custom function in JANET overcomes the problem. In this paper, JANET is introduced as a classifier for mining the long-term temporal patterns, and the result proves the breathtaking performance of the proposed method.

Key words: radar signal deinterleaving, JANET, deinterleaving, pulse stream

中图分类号:

TN959

姜在阳, 孙思月, 李华旺, 梁广. 一种基于JANET模型的雷达信号分选方法[J]. 中国科学院大学学报, 2021, 38(6): 825-831.

JIANG Zaiyang, SUN Siyue, LI Huawang, LIANG Guang. A method for deinterleaving based on JANET[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(6): 825-831.

参考文献

[1] Matuszewski J. The analysis of modern radar signals parameters in electronic intelligence system[C]//2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET). Lviv,Ukraine:IEEE Press, 2016:298-302.
[2] Matuszewski J. The specific radar signature in electronic recognition system[J]. Przegląd Elektrotechniczny, 2013, 89(7):236-239.
[3] Matuszewski J. The radar signature in recognition system database[C]//2012 19th International Conference on Microwaves, Radar & Wireless Communications. Warsaw Poland:IEEE Press, 2012:617-622.
[4] 张敏,郭福成,李腾,等. 旋转长基线干涉仪测向方法及性能分析[J]. 电子学报,2013,41(12):2422-2429.
[5] Peng H M, Chang E R, Wang L S. Rotation method for direction finding via GPS carrier phases[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(1):72-84.
[6] Mardia H K. New techniques for the deinterleaving of repetitive sequences[J]. IEE Proceedings F (Radar and Signal Processing), 1989, 136(4):149-154.
[7] Milojević D J, Popović B M. Improved algorithm for the deinterleaving of radar pulses[J].IEE Proceedings F (Radar and Signal Processing), 1992, 139(1):98-104.
[8] Orsi R J, Moore J B, Mahony R E. Spectrum estimation of interleaved pulse trains[J]. IEEE Transactions on Signal Processing, 1999, 47(6):1646-1653.
[9] Nishiguchi K, Kobayashi M. Improved algorithm for estimating pulse repetition intervals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2):407-421.
[10] Liu Z M, Yu P S. Classification, denoising, and deinterleaving of pulse streams with recurrent neural networks[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(4):1624-1639.
[11] Westhuizen J V D, Lasenby J. The unreasonable effectiveness of the forget gate[EB/OL]. (2018-09-13)[2019-09-20].http://arxiv.org/pdf/1804.04849.pdf.
[12] Wiley R G. ELINT the interception and analysis of radar signals[M]. Boston:Artech House, 2006:478.
[13] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[EB/OL]. (2013-09-07)[2019-09-20].http://arxiv.org/pdf/1301.3781.pdf.
[14] Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook[M]//Recommender systems handbook. Boston, MA:Springer, 2011:1-35.
[15] Petrov N, Jordanov I, Roe J. Radar emitter signals recognition and classification with feedforward networks[J]. Procedia Computer Science, 2013, 22:1192-1200.
[16] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[17] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2):157-166.

一种基于JANET模型的雷达信号分选方法

A method for deinterleaving based on JANET

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

访问统计

联系我们

[1]	尹迪, 韩冰, 孙吉利, 赵良波, 洪文, 胡玉新. 天线扫描对TOPSAR辐射影响的校正方法[J]. 中国科学院大学学报, 2021, 38(6): 791-799.
[2]	廖洪海, 何为, 林水洋. 基于PE的MEEMD筛选的LFMCW雷达心率估计算法[J]. 中国科学院大学学报, 2021, 38(5): 666-677.
[3]	田东, 禹卫东. 电离层色散效应对星载SAR成像的影响[J]. 中国科学院大学学报, 2019, 36(3): 376-384.
[4]	梁健, 张润宁, 王大伟, 罗荣蒸. 敏捷SAR卫星视频成像模式的姿态机动策略[J]. 中国科学院大学学报, 2019, 36(1): 125-130.
[5]	陈立福, 王思雨, 袁志辉, 王静, 邢学敏, 武鸿. 非线性ECS自配准成像算法中相位保持精度分析[J]. 中国科学院大学学报, 2017, 34(5): 640-646.
[6]	陈立福, 林月冠, 彭曙蓉, 李红英. 一种用于机载双天线InSAR系统的实时DEM生成方法及实现[J]. 中国科学院大学学报, 2014, 31(5): 678-684.
[7]	韩冰, 张永军, 刘佳音, 张问一. 斜视滑动聚束SAR成像的ECS算法[J]. 中国科学院大学学报, 2012, 29(5): 674-680.
[8]	陈立福, 汪丙南, 向茂生. 一种干涉条纹快速滤波算法[J]. 中国科学院大学学报, 2011, 28(3): 349-354.
[9]	韩松涛, 韦立登, 向茂生. 一种适用于双天线InSAR系统的成像定位算法[J]. 中国科学院大学学报, 2010, 27(2): 218-221.
[10]	商建, 杨汝良. 星载测雨雷达与地基雷达数据的空间匹配分析[J]. 中国科学院大学学报, 2009, 26(2): 203-208.
[11]	陈洁;　方广有;　李芳. 超宽带穿墙雷达非相关成像方法[J]. 中国科学院大学学报, 2007, 24(6): 829-834.