基于极大似然估计的非理想场景雷达信号分选算法

doi:10.12305/j.issn.1001-506X.2024.07.11

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (7): 2276-2284.doi: 10.12305/j.issn.1001-506X.2024.07.11

• 传感器与信号处理 • 上一篇

基于极大似然估计的非理想场景雷达信号分选算法

陈柯宇¹^,², 杨健²^,³, 张伟¹^,⁴^,*, 孙国敏¹, 邵怀宗¹^,²

1. 电子科技大学信息与通信工程学院, 四川成都 611731
2. 电磁空间认知与智能控制技术实验室, 北京 100089
3. 北京理工大学网络空间安全学院, 北京 100089
4. 电磁空间安全全国重点实验室, 四川成都 610036

收稿日期:2023-03-28 出版日期:2024-06-28 发布日期:2024-07-02
通讯作者: 张伟
作者简介:陈柯宇(1999—), 男, 硕士研究生, 主要研究方向为雷达信号处理
杨健(1982—), 男, 研究员, 博士研究生, 主要研究方向为群体电磁博弈
张伟(1985—), 男, 高级工程师, 博士研究生, 主要研究方向为非合作信号与信息处理、电子对抗
孙国敏(1990—), 女, 助理研究员, 博士, 主要研究方向为辐射源智能识别
邵怀宗(1967—), 男, 教授, 博士, 主要研究方向为无线通信、电子对抗
基金资助:
国家自然科学基金(U20B2070)

Maximum likelihood estimation based deinterleaving algorithm of radar signal in non-ideal scenarios

Keyu CHEN¹^,², Jian YANG²^,³, Wei ZHANG¹^,⁴^,*, Guomin SUN¹, Huaizong SHAO¹^,²

1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2. Laboratory of Electromagnetic Space Cognition and Intelligent Control, Beijing 100089, China
3. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100089, China
4. National Key Laboratory of Electromagnetic Space Security, Chengdu 610036, China

Received:2023-03-28 Online:2024-06-28 Published:2024-07-02
Contact: Wei ZHANG

摘要/Abstract

摘要：

目前, 已有研究针对理想条件下的雷达脉冲信号分选问题进行了详细阐述, 但是缺乏杂散脉冲和缺失脉冲两种非理想情景下的模型表征。为解决这一问题, 提出了一种基于极大似然估计的非理想场景雷达信号分选算法。该算法通过修正似然因子来表征杂散脉冲和缺失脉冲现象, 提高在复杂场景下的分选准确率。当部分雷达先验信息已知时, 所提算法模型具有更好的分选效果。仿真实验结果表明, 与已有的极大似然模型和深度学习算法相比, 所提算法在分选准确率上有显著提升, 具有较高的应用价值。

关键词: 雷达信号分选, 脉冲重复间隔, 电子侦察, 极大似然估计

Abstract:

The existing research has elaborated on the problem of deinterleaving of radar pulse signal under ideal conditions, but lacks the model representation under two non-ideal scenarios, spurious pulses and lost pulses. To solve this problem, a deinterleaving algorithm of radar signals in non-ideal scenarios based on maximum likelihood estimation is proposed, which characterizes spurious pulse and lost pulse phenomena by modifying the likelihood factor, so as to improve the deinterleaving accuracy in complex scenarios. When part of the radar prior information is known, the proposed algorithm model has better deinterleaving effect. Simulation experiment results show that, compared with the existing maximum likelihood model and the deep learning algorithm, the proposed algorithm has a significant improvement in the deinterleaving accuracy and has high application value.

Key words: deinteleaving of radar signal, pulse repetition interval, electronic reconnaissance, maximum likelihood estimataion

中图分类号:

TN911.7

陈柯宇, 杨健, 张伟, 孙国敏, 邵怀宗. 基于极大似然估计的非理想场景雷达信号分选算法[J]. 系统工程与电子技术, 2024, 46(7): 2276-2284.

Keyu CHEN, Jian YANG, Wei ZHANG, Guomin SUN, Huaizong SHAO. Maximum likelihood estimation based deinterleaving algorithm of radar signal in non-ideal scenarios[J]. Systems Engineering and Electronics, 2024, 46(7): 2276-2284.

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参考文献 21

1	SHAPEROS.Introduction to modern EW systems, (de Martino, A.)[Book review][J].IEEE Aerospace and Electronic Systems Magazine,2019,34(8):62-63. doi: 10.1109/MAES.2019.2921175
2	姜宏志,赵闯,胡德秀,等.基于时差多参的单脉冲信号实时配对分选[J].电子学报,2021,49(3):566-572.
	JIANGH Z,ZHAOC,HUD X,et al.Real-time deinterleaving algorithm for single pulse signal based on TDOAs and multi-parameter information[J].Acta Electronica Sinica,2021,49(3):566-572.
3	李合生,韩宇,蔡英武,等.雷达信号分选关键技术研究综述[J].系统工程与电子技术,2005,27(12):2035-2040.
	LIH S,HANY,CAIY W,et al.Overview of the crucial technology research for radar signal sorting[J].Systems Engineering and Electronics,2005,27(12):2035-2040.
4	APFELDS,CHARLISHA.Recognition of unknown radar emitters with machine learning[J].IEEE Trans.on Aerospace and Electronic Systems,2021,57(6):4433-4447. doi: 10.1109/TAES.2021.3098125
5	石荣,吴聪.基于PRI信息的雷达脉冲信号分选技术研究综述[J].电讯技术,2020,60(1):112-120.
	SHIR,WUC.Review on technology research about radar pulse signal deinterleaving based on PRI information[J].Telecommunication Engineering,2020,60(1):112-120.
6	MARDIAH K.New techniques for the deinterleaving of repetitive sequences[J].IEE Proceedings F (Radar and Signal Processing),1989,136(4):149-154. doi: 10.1049/ip-f-2.1989.0025
7	MILOJEVICD J,POPOVICB M.Improved algorithm for the deinterleaving of radar pulses[J].IEE Proceedings F (Radar and Signal Processing),1992,139(1):98-104. doi: 10.1049/ip-f-2.1992.0012
8	NISHIGUCHIK,KOBAYASHIM.Improved algorithm for estimating pulse repetition intervals[J].IEEE Trans.on Aerospace and Electronic Systems,2000,36(2):407-421. doi: 10.1109/7.845217
9	LIUY C,ZHANGQ Y.Improved method for deinterleaving radar signals and estimating PRI values[J].IET Radar, Sonar & Na-vigation,2018,12(5):506-514.
10	CHENGW H,ZHANGQ Y,DONGJ M,et al.An enhanced algorithm for deinterleaving mixed radar signals[J].IEEE Trans.on Aerospace and Electronic Systems,2021,57(6):3927-3940. doi: 10.1109/TAES.2021.3087832
11	YOUNGJ,HØST-MADSENA,NOSALE M.Deinterleaving of mixtures of renewal processes[J].IEEE Trans.on Signal Processing,2018,67(4):885-898.
12	ZHUM T,LIY J,WANGS F.Model-based time series clustering and interpulse modulation parameter estimation of multifunction radar pulse sequences[J].IEEE Trans.on Aerospace and Electronic Systems,2021,57(6):3673-3690. doi: 10.1109/TAES.2021.3082660
13	ZHUM T,WANGS F,LIY J.Model-based representation and deinterleaving of mixed radar pulse sequences with neural machine translation network[J].IEEE Trans.on Aerospace and Electronic Systems,2021,58(3):1733-1752.
14	AHMADIM,MOHAMEDPOURK.PRI modulation type recognition using level clustering and autocorrelation[J].American Journal of Signal Processing,2012,2(5):83-91.
15	YUANS,KANGS Q,SHANGW X,et al.Reconstruction of radar pulse repetition pattern via semantic coding of intercepted pulse trains[J].IEEE Trans.on Aerospace and Electronic Systems,2022,59(1):394-403.
16	隋金坪,刘振,刘丽,等.雷达辐射源信号分选研究进展[J].雷达学报,2022,11(3):418-433.
	SUIJ P,LIUZ,LIUL,et al.Progress in radar emitter signal deinterleaving[J].Journal of Radars,2022,11(3):418-433.
17	LIUZ M.Online pulse deinterleaving with finite automata[J].IEEE Trans.on Aerospace and Electronic Systems,2019,56(2):1139-1147.
18	LIX Q,LIUZ M,HUANGZ T.Deinterleaving of pulse streams with denoising autoencoders[J].IEEE Trans.on Aerospace and Electronic Systems,2020,56(6):4767-4778.
19	LIUZ M.Pulse deinterleaving for multifunction radars with hierarchical deep neural networks[J].IEEE Trans.on Aerospace and Electronic Systems,2021,57(6):3585-3599.
20	LIUZ M,YUP S.Classification, denoising, and deinterleaving of pulse streams with recurrent neural networks[J].IEEE Trans.on Aerospace and Electronic Systems,2019,55(4):1624-1639.
21	WANGH Y,ZHUM T,FANR Z,et al.Parametric model-based deinterleaving of radar signals with non-ideal observations via maximum likelihood solution[J].IET Radar, Sonar & Navigation,2022,16(8):1253-1268.

场景序号	调制类型	参数
1	抖动PRI	Θ=(31, 1²)
		Θ=(55, 1²)
		Θ=(67, 1²)
2	滑变PRI	Θ=(16, 1²)
		Θ=(22, 1²)
		Θ=(9, 1²)
3	参差PRI	B=(43, 34, 67, 56)
		B=(47, 61, 55)
		B=(23, 76, 59)
4	正弦PRI	Θ=(3, 0.02, 40, 0.5², 0)
		Θ=(4, 0.015, 35, 0.5², 0)
		Θ=(5, 0.01, 30, 0.5², 0)

场景	杂散脉冲率
场景	0	0.05	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.45	0.50
抖动PRI	0.300	0.363	0.374	0.308	0.311	0.276	0.303	0.256	0.303	0.290	0.289
正弦PRI	0.392	0.360	0.432	0.445	0.385	0.294	0.382	0.385	0.343	0.250	0.383
滑变PRI	0.362	0.359	0.346	0.297	0.238	0.275	0.212	0.236	0.080	-0.002	0.053
参差PRI	0.258	0.183	0.336	0.248	0.250	0.277	0.206	0.189	0.147	0.177	0.183

指标	对比算法	抖动PRI	正弦PRI	滑变PRI	参差PRI
召回率	SPML	0.928	0.970	0.957	0.944
召回率	DAE	0.640	0.725	0.803	0.920
精度	SPML	0.915	0.967	0.899	0.950
精度	DAE	0.320	0.528	0.490	0.381

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基于极大似然估计的非理想场景雷达信号分选算法

Maximum likelihood estimation based deinterleaving algorithm of radar signal in non-ideal scenarios

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