Deinterleaving of radar pulse based on implicit feature

doi:10.23919/JSEE.2023.000032

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (6): 1537-1549.doi: 10.23919/JSEE.2023.000032

收稿日期:2021-11-10 接受日期:2022-10-08 出版日期:2023-12-18 发布日期:2023-12-29

Deinterleaving of radar pulse based on implicit feature

Qiang GUO¹(), Long TENG¹(), Xinliang WU²(), Liangang QI¹^,*(), Wenming SONG²()

¹ College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
² China National Aeronautical Radio Electronics Research Institute, Shanghai 200030, China

Received:2021-11-10 Accepted:2022-10-08 Online:2023-12-18 Published:2023-12-29
Contact: Liangang QI E-mail:guoqiang@hrbeu.edu.cn;tenglong@hrbeu.edu.cn;wuxinliang51@163.com;qiliangang@hrbeu.edu.cn;fate1530@126.com
About author:
GUO Qiang was born in 1972. He received his B.S., M.S., and Ph.D. degrees in information and communication engineering from Harbin Engineering University, China, in 1994, 2003, and 2007 respectively. In 2009, he received the National 100 Excellent Doctoral Degree Dissertation Candidate Nomination. He is currently a full professor with the College of Information and Communication Engineering. He is a review expert of the Science and Technology Commission of the Military Commission, the Degree and Postgraduate Education Center of the Ministry of Education, the National Natural Science Foundation of China, and the National Science and Technology Award. His research interests include electronic countermeasure, machine learning, radar signals sorting, and recognition. E-mail: guoqiang@hrbeu.edu.cn

TENG Long was born in 1994. He received his B.E. degree in electronic information engineering from Shaanxi University of Technology, Shaanxi, China, in 2016. He is pursuing his Ph.D. degree in information and communication engineering, Harbin Engineering University. His research interests include radar signals sorting, deep learning, and information fusion. E-mail: tenglong@hrbeu.edu.cn

WU Xinliang was born in 1982. He received his B.E. degree in detection guidance and control technology from Northwestern Polytechnical University, Shaanxi, China, in 2003. He received his M.S. degree from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2013. His research interests include avionics integrated technology, information fusion and image processing. E-mail: wuxinliang51@163.com

QI Liangang was born in 1990. He received his B.S. and Ph.D. degrees in information and communication engineering from Harbin Engineering University, China, in 2013 and 2018, respectively. He has been a lecturer with Harbin Engineering University since 2018. His research interests include electronic countermeasure, machine learning, radar target recognition, and radar signal processing. E-mail: qiliangang@hrbeu.edu.cn

SONG Wenming was born in 1988. He received his B.S. and M.S. degrees in information and communication engineering from Harbin Engineering University, China, in 2011 and 2014 respectively. His research interests include avionics integrated technology, information fusion, radar signals sorting and deep learning. E-mail: fate1530@126.com
Supported by:
This work was supported by the National Major Research & Development project of China (2018YFE0206500), the National Natural Science Foundation of China (62071140), the Program of China International Scientific and Technological Cooperation (2015DFR10220), and the Technology Foundation for Basic Enhancement Plan (2021-JCJQ-JJ-0301).

摘要/Abstract

Abstract:

In the complex countermeasure environment, the pulse description words (PDWs) of the same type of multi-function radar emitters are similar in multiple dimensions. Therefore, it is difficult for conventional methods to deinterleave such emitters. In order to solve this problem, a pulse deinterleaving method based on implicit features is proposed in this paper. The proposed method introduces long short-term memory (LSTM) neural networks and statistical analysis to mine new features from similar PDW features, that is, the variation law (implicit features) of pulse sequences of different radiation sources over time. The multi-function radar emitter is deinterleaved based on the pulse sequence variation law. Statistical results show that the proposed method not only achieves satisfactory performance, but also has good robustness.

Key words: multi-functional radars of the same type, pulse deinterleaving, pulse amplitude, implicit feature, long short-term memory (LSTM) neural networks

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(6): 1537-1549.

Qiang GUO, Long TENG, Xinliang WU, Liangang QI, Wenming SONG. Deinterleaving of radar pulse based on implicit feature[J]. Journal of Systems Engineering and Electronics, 2023, 34(6): 1537-1549.

图/表 18

参考文献 32

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Item	Formula
Centroid	Equations (9) and (10)
PA difference between centroid pulses	$\left\| {{{\rm{PA}}_{{{\rm{FR}}_{m + 1} } } } - {{\rm{PA}}_{{{\rm{FR}}_m} } } } \right\|$
TOA difference between centroid pulses	${{\rm{TOA}}_{{{\rm{FR}}_{m + 1} } } } - {{\rm{TOA}}_{{{\rm{FR}}_m} } }$
Peak of PA envelope	${{\rm{PA}}_{{{\rm{FR}}_{m - 1} } } } < {{\rm{PA}}_{{{\rm{FR}}_m} } } < {{\rm{PA}}_{{{\rm{FR}}_{m + 1} } } }$
Implicit feature	LSTM

Item	Value
Functional state	Track and search (TAS)
Transmitters power/W	12
Maximum gain/dB	38
First sidelobe level/dB	−34
Average sidelobe level/dB	−44
3 dB beam width (azimuth)	2°
3 dB beam width (elevation)	2°
Antenna scanning rate/(°/s)	50
Azimuth scanning/(°)	±15
Elevation scanning/(°)	±4
RF/MHz	[9400,9800], pseudo-random agility
PRI/μs	[30,50], group stagger
DOA/(°)	36
PW/ns	1 300

Parameter	Unit	Method
Input layer	1	−
LSTM layer	128	−
Fully connected layer	256	−
Output layer	1	−
Optimization	−	Adam
Loss function	−	Root mean square error (RMSE)

Parameter	Unit	Method
Input layer	4	−
LSTM layer	256	−
LSTM layer	256	−
Fully connected layer	512	−
Output layer	1	−
Optimization	−	Adam
Loss function	−	RMSE

Criterion	Results in [20]	DNN	SVR	Improved method
Number of total pulses	584873	584873	584873	584873
Number of correct pulses	478101	502371	503565	555759
Number of false pulses	106772	82502	81308	29114
Accuracy/%	81.74	85.90	86.10	95.02
Computation time/s	29.97	40.78	38.85	43.49

Deinterleaving of radar pulse based on implicit feature

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Scenario	Missing	Jitter PRI	Noise	Probabilities of correct deinterleaving
1	20	20	10	92.67
2	40	40	10	92.37
3	50	50	10	91.86
4	50	50	20	90.04
5	50	50	30	87.36
6	50	50	40	85.89
7	50	50	50	83.43