Intelligent optimization methods of phase-modulation waveform

doi:10.23919/JSEE.2022.000089

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 916-923.doi: 10.23919/JSEE.2022.000089

收稿日期:2021-01-21 接受日期:2022-03-21 出版日期:2022-08-30 发布日期:2022-08-30

Intelligent optimization methods of phase-modulation waveform

Jianwei SUN^1,²(), Chao WANG^1,^2,*(), Qingzhan SHI^1,²(), Wenbo REN^1,²(), Zekun YAO^1,²(), Naichang YUAN^1,²()

¹ College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
² State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

Received:2021-01-21 Accepted:2022-03-21 Online:2022-08-30 Published:2022-08-30
Contact: Chao WANG E-mail:1195085653@qq.com;sywangc@163. com;qingzhan1990@163.com;464735596@qq.com;13177654355@163.com;yncwww@qq.com
About author:|SUN Jianwei was born in 1997. He received his B.S. degree from the National University of Defense Technology, Changsha, China, in 2019, where he is currently pursuing his M.S degree. His research interests are radio frequency and microwave technology. E-mail: 1195085653@qq.com||WANG Chao was born in 1977. He received his Ph.D. degree from the National University of Defense Technology (NUDT), Changsha, in 2007. He is currently an associate professor with NUDT. His research interest is electronic system design. E-mail: sywangc@163. com||SHI Qingzhan was born in 1990. He received his M.S. and Ph.D. degrees from the National University of Defense Technology (NUDT), Changsha, in 2015 and 2019, respectively. He is currently a lecturer with the NUDT. His research interest is signal processing. E-mail: qingzhan1990@163.com||REN Wenbo was born in 1995. He received his B.S. degree from the Southwest University of Science and Technology, Sichuan, China, in 2018. He is currently pursuing his M.S. degree in National University of Defense Technology. His research interests are microwave and millimeter-wave technology. E-mail: 464735596@qq.com||YAO Zekun was born in 1998. He received his B.S. degree from the National University of Defense Technology, Changsha, China, in 2020, where he is currently pursuing his M.S. degree. His research interests are radio frequency and microwave technology. E-mail: 13177654355@163.com||YUAN Naichang was born in 1965. He received his M.S. and Ph.D. degrees in electronic science and technology from the University of Electronic Science and Technology of China in 1991 and 1994, respectively. He is currently a professor with National University of Defense Technology. His research interests include signal processing and electronic system design. E-mail: yncwww@qq.com

摘要/Abstract

Abstract:

With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance. Genetic algorithm (GA) and particle swarm optimization (PSO) are used to simulate the intelligent optimization of interrupted-sampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.

Key words: waveform optimization, intelligent optimization, phase-modulation, genetic algorithm (GA), particle swarm optimization (PSO)

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 916-923.

Jianwei SUN, Chao WANG, Qingzhan SHI, Wenbo REN, Zekun YAO, Naichang YUAN. Intelligent optimization methods of phase-modulation waveform[J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 916-923.

图/表 10

参考文献 36

1	YAO Y, ZHAO J H, WU L N Waveform optimization for target estimation by cognitive radar with multiple antennas. Sensors, 2018, 18 (6): 1743. doi: 10.3390/s18061743
2	WEI Z H, LIU Z, PENG B, et al ECCM scheme against interrupted sampling repeater jammer based on parameter-adjusted waveform design. Sensors, 2018, 18 (4): 1141. doi: 10.3390/s18041141
3	ZHAO Z X, YUAN J L, LI M X Research on adaptive waveform optimization design of anti-jamming radar. Journal of Physics: Conference Series, 2020, 1650 (2): 022111. doi: 10.1088/1742-6596/1650/2/022111
4	Defense Advanced Research Projects Agency. Behavioral learning for adaptive electronic warfare (BLADE) program homepage. http://www.darpa.mil.
5	Defense Advanced Research Projects Agency. Adaptive radar countermeasures (ARC) program homepage. http://www.darpa.mil.
6	WANG S F, BAO Y F, LI Y The architecture and technology of cognitive electronic warfare. Scientia Sinica Informations, 2018, 48 (12): 1603- 1613, 1709. doi: 10.1360/N112018-00153
7	ZHANG H W, XIE J W, LU W L, et al A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar. Frontiers of Information Technology and Electronic Engineering, 2017, 18 (11): 1806- 1816. doi: 10.1631/FITEE.1601358
8	WANG Y K, ZHENG S Y Research on radar task scheduling with power constraint. The Journal of Engineering, 2019, 2019 (19): 5990- 5993. doi: 10.1049/joe.2019.0040
9	ZHANG H W, XIE J W, GE J A, et al Finite sensor selection algorithm in distributed MIMO radar for joint target tracking and detection. Journal of Systems Engineering and Electronics, 2020, 31 (2): 290- 302. doi: 10.23919/JSEE.2020.000007
10	XUE H, ZHANG T, WANG R, et al Application of intelligent optimization technology in radar system. Modern Radar, 2020, 42 (2): 1- 6.
11	GUO Y, SUO Z Y, WANG T T, et al Parameter optimization design method of missile-borne SAR system. Systems Engineering and Electronics, 2020, 42 (7): 1478- 1483.
12	LI K, JIU B, LIU H W, et al Waveform design for cognitive radar in presence of jammer using Stackelberg game. The Journal of Engineering, 2019, 2019 (21): 7581- 7584. doi: 10.1049/joe.2019.0621
13	JIANG H Q, ZHANG Y R, XU H Y Optimal allocation of cooperative jamming resource based on hybrid quantum-behaved particle swarm optimisation and genetic algorithm. IET Radar Sonar & Navigation, 2017, 11 (1): 185- 192.
14	ZHANG L, SHI G Q, GENG X T. Blanket jamming targets assignment based on adaptive genetic algorithm. Proc. of the IEEE International Conference on Cybernetics and Intelligent Systems, 2019: 171–175.
15	LUO Z Y, DENG M, YAO Z Q, et al Distributed blanket jamming resource scheduling for satellite navigation based on particle swarm optimization and genetic algorithm. Proc. of the IEEE 20th International Conference on Communication Technology, 2020, 611- 616.
16	SHIN J J, BANG H UAV path planning under dynamic threats using an improved PSO algorithm. International Journal of Aerospace Engineering, 2020, 2020 (10): 1- 17.
17	QI F, HONG C S, GAO R Z Path planning of stand-off jamming electronic warfare aircraft. Proc. of the 39th Chinese Control Conference, 2020, 6917- 6922.
18	JIA R, ZHANG T X, WANG Y H, et al. An intelligent range gate pull-off (RGPO) jamming method. Proc. of the International Conference on UK-China Emerging Technologies, 2020. DOI: 10.1109/UCET51115.2020.9205386.
19	JIANG J W, WANG H Y, WU Y H, et al Intermittent sampling repeater jamming based on multiple phases sectionalized modulation. Systems Engineering and Electronics, 2019, 41 (7): 1450- 1458.
20	JIANG J W, WU Y H, WANG H Y, et al Optimization algorithm for multiple phases sectionalized modulation jamming based on particle swarm optimization. Electronics, 2019, 8 (2): 160- 185. doi: 10.3390/electronics8020160
21	JIANG J W, WANG H Y, WU Y H, et al Optimization method for multiple phases sectionalized modulation jamming against linear frequency modulation radar based on a genetic algorithm. IEEE Access, 2020, 8, 88777- 88792. doi: 10.1109/ACCESS.2020.2994084
22	WANG X S, LIU J C, ZHANG W M, et al Mathematic principles of interrupted-sampling repeater jamming (ISRJ). Science in China Series F: Information Sciences, 2007, 50 (1): 113- 123. doi: 10.1007/s11432-007-2017-y
23	LIU Z. Jamming technique for countering LFM pulse compression radar based on digital radio frequency memory. Changsha, China: National University of Defense Technology, 2006. (in Chinese)
24	TAI N, PAN Y J, ZHANG D P, et al Quasi-coherent noise jamming based on interrupted-sampling and pseudo-random serials phase-modulation. Proc. of the Progress in Electromagnetics Research Symposium, 2014, 1001- 1005.
25	SHI Q Z, WU S, HUANG J J, et al. A novel jamming method against LFM radar using pseudo-random code phase modulation. Proc. of the IEEE International Conference on Signal Processing, Communications and Computing, 2017. DOI: 10.1109/ICSPCC2017.8242394.
26	SHI Q Z, TAI N, WANG C On deception jamming for countering LFM radar based on periodic 0-π phase modulation. International Journal of Electronics and Communications, 2018, 83 (1): 245- 252. doi: 10.1016/j.aeue.2017.09.010
27	SHI Q Z, WANG C, HUANG J J, et al Multiple targets deception jamming against ISAR based on periodic 0-π phase modulation. IEEE Access, 2018, 6, 3539- 3548. doi: 10.1109/ACCESS.2018.2792477
28	SHI Q Z, HUANG J J, XIE T, et al An active jamming method against ISAR based on periodic binary phase modulation. IEEE Sensors Journal, 2019, 19 (18): 7950- 7960. doi: 10.1109/JSEN.2019.2905557
29	AI T F, SU Y H, KOU M X Study on the influence of intermittent phase modulation on the spectrum shift characteristics of radar signals. IOP Conference Series Earth and Environmental Science, 2020, 440, 052088. doi: 10.1088/1755-1315/440/5/052088
30	WU Z L, XIONG X, YU G W, et al The suppression interference threshold of multi-false target under cell average constant false alarm rate detector. Electronic Information Warfare Technology, 2018, 33 (1): 49- 53.
31	LIU X, LI D S Analysis of cooperative jamming against pulse compression radar based on CFAR. Journal on Advances in Signal Processing, 2018, 2018 (1): 69.
32	XIA X Y, HAO D L, YAN L, et al Optimal waveform design for smart jamming focused on CA-CFAR. Proc. of the International Conference on Computer Network, Electronic and Automation, 2017, 374- 378.
33	KATOCH S, CHAUHAN S S, KUMAR V A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021, 80 (5): 8091- 8126. doi: 10.1007/s11042-020-10139-6
34	JAIN N K, NANGIA U, JAIN J A review of particle swarm optimization. Journal of The Institution of Engineers (India): Series B, 2018, 99 (4): 407- 411. doi: 10.1007/s40031-018-0323-y
35	YANG B W, QIAN W Y Summary on improved inertia weight strategies for particle swarm optimization algorithm. Journal of Bohai University (Natural Science Edition), 2019, 40 (3): 274- 288.
36	FREITAS D, LOPES L G, MORGADO-DIAS F Particle swarm optimisation: a historical review up to the current developments. Entropy, 2020, 22 (3): 362- 397. doi: 10.3390/e22030362

Parameter	Lower limit	Upper limit	Precision
Code width	0.1	4	0.1
Sampling time	1	10	0.5
Forwarding time	1	7	1
Lead time	0	10	0.5
Other constraints	$ {T_c} \leqslant {\text{0}}{\text{.4}}\tau $

Parameter	Detailed parameter
Radar signal 1	LFM (1 MHz, 64 μs)
Radar signal 2	LFM (1 MHz, 128 μs)
Radar signal 3	LFM (8 MHz, 64 μs)
Radar signal 4	PCM (2 MHz, 127 × 0.5 μs)
Radar signal 5	NLFM (8 MHz, 64 μs)
False alarm probability	10^?3
Protection unit	4
Average unit	16
Pseudo-random sequence	M sequence (511-bit)

Signal	Initial optimal	Global optimal
Signal 1	5.83	8.54
Signal 2	5.12	7.04
Signal 3	2.94	7.00
Signal 4	?2.83	2.76
Signal 5	4.59	7.06

Intelligent optimization methods of phase-modulation waveform

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