Automatic radar antenna scan type recognition based on limited penetrable visibility graph

doi:10.23919/JSEE.2021.000037

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (2): 437-446.doi: 10.23919/JSEE.2021.000037

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Automatic radar antenna scan type recognition based on limited penetrable visibility graph

Songtao LIU*(), Zhenshuo LEI(), Yang GE(), Zhenming WEN()

¹ Department of Information System, Dalian Naval Academy, Dalian 116018, China

Received:2020-05-27 Online:2021-04-29 Published:2021-04-29
Contact: Songtao LIU E-mail:navylst@163.com;448051487@qq.com;gy0720@139.com;935838465@qq.com
About author:|LIU Songtao was born in 1978. He received his B.S. degree in aviation radar, M.S. degree and Ph.D. degree in signal and information processing from Naval Aeronautical Engineering Institute, in 2000, 2003 and 2006, respectively. He joined Dalian Naval Academy in 2006 where he is now an associate professor in the Department of Information System. From 2010 to 2012, he was a postdoctoral researcher at the Postdoctoral Station of Information and Communication Engineering, Dalian University of Technology. He has published over 80 papers in journals and conference proceedings. His research interests include image processing, optoelectronic engi-neering, and electronic countermeasures. E-mail: navylst@163.com||LEI Zhenshuo was born in 1996. He received his B.S. degree from the Beijing Jiaotong University, Beijing, China, in 2018. He is currently pursuing his M.S. degree in electronics and information engineering from Dalian Naval Academy. His research interests include electronic countermeasures, and machine learning. E-mail: 448051487@qq.com||GE Yang was born in 1992. She received her B.S. degree in electronic information science and technology from Xi’an University of Technology in 2015. She is currently pursuing her master’s degree in electronics and information engineering from Dalian Naval Academy. Her research interests include electronic countermeasures technology and application. E-mail: gy0720@139.com||WEN Zhenming was born in 1995. He received his B.S. degree in electronic information engineering from Dalian University of Technology, in 2018. Now he is a postgraduate student of electronics and information engineering in Dalian Naval Academy. He is mainly engaged in the research of multi-sensor target recognition and tracking technology. E-mail: 935838465@qq.com
Supported by:
This work was supported by the China Postdoctoral Science Foundation (2015M572694; 2016T90979);This work was supported by the China Postdoctoral Science Foundation (2015M572694; 2016T90979)

Abstract

Abstract:

To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type, a recognition method for radar antenna scan types based on limited penetrable visibility graph (LPVG) is proposed. Firstly, seven types of radar antenna scans are analyzed, which include the circular scan, sector scan, helical scan, raster scan, conical scan, electromechanical hybrid scan and two-dimensional electronic scan. Then, the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network, and the feature parameters are extracted. Finally, the recognition result is obtained by using a support vector machine (SVM) classifier. The experimental results show that the recognition accuracy and noise resistance of this new method are improved, where the average recognition accuracy for radar antenna type is at least 90% when the signal-to-noise ratio (SNR) is 5 dB and above.

Key words: antenna scan type, limited penetrable visibility graph (LPVG), support vector machine (SVM), cognitive electronic warfare

Songtao LIU, Zhenshuo LEI, Yang GE, Zhenming WEN. Automatic radar antenna scan type recognition based on limited penetrable visibility graph[J]. Journal of Systems Engineering and Electronics, 2021, 32(2): 437-446.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 1

References 16

1	DUTT R, BALORIA A, RADHAKRISHNA E Discrete wavelet transform based unsupervised underdetermined blind source separation methodology for radar pulse deinterleaving using antenna scan pattern. IET Radar, Sonar and Navigation, 2019, 13, 1350- 1358. doi: 10.1049/iet-rsn.2018.5525
2	YAR E, KOCAMIS M B, ORDUYILMAZ A. A complete framework of radar pulse detection and modulation classification for cognitive EW. Proc. of the 27th European Signal Processing Conference, 2019: 1−5.
3	AYZGOK S, ERDEM C, OZTURK M T Automatic antenna scan type classification for next-generation electronic warfare receivers. IET Radar, Sonar and Navigation, 2018, 12, 466- 474. doi: 10.1049/iet-rsn.2017.0354
4	LI C, WANG W, SHI L F, et al Automatic recognition method of radar antenna scan type. Journal of National University of Defense Technology, 2014, 36 (3): 156- 163.
5	ZHANG X Q, HAO C Y, ZHANG S S, et al. Characterization and identification of active electronically scanned array radar. Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2017: 2318−2321.
6	KIM Y H, KIM W J, SONG K H. Modeling of a radar signal for scan pattern. Proc. of the IEEE Military Communications Conference, 2009: 1−6.
7	WAN T, FU X Y, JIANG K L, et al Radar antenna scan pattern intelligent recognition using visibility graph. IEEE Access, 2019, 7, 175628- 175641. doi: 10.1109/ACCESS.2019.2957769
8	ZHOU T T, JIN N D, GAO Z K Limited penetrable visibility graph for establishing complex network from time series. Acta Physica Sinica, 2012, 61 (3): 030506. doi: 10.7498/aps.61.030506
9	BI D P, HUANG J C, JIANG Q X. Research on radar antenna scanning simulation. Proc. of the 12th Annual Conference on Electronic Countermeasure, 2001: 355−360. (in Chinese)
10	JEFFREY T. Phased-array radar design: application of radar fundamentals. Raleigh: SciTech Publishing, Inc, 2009.
11	GAO Y Y, YU D J Total variation on horizontal visibility graph and its application to rolling bearing fault diagnosis. Mechanism and Machine Theory, 2020, 147, 103768. doi: 10.1016/j.mechmachtheory.2019.103768
12	GAO Z K, FENG Y H, MA C, et al Disrupted time-dependent and functional connectivity brain network in alzheimer’s disease: a resting-state fMRI study based on visibility graph. Current Alzheimer Research, 2020, 17 (1): 69- 79. doi: 10.2174/1567205017666200213100607
13	LIU K S, WENG T F, GU C G, et al Visibility graph analysis of Bitcoin price series. Physica A: Statistical Mechanics and its Applications, 2020, 538, 122952. doi: 10.1016/j.physa.2019.122952
14	LACASA L, LUQUE B, LUQUE J, et al The visibility graph: a new method for estimating the Hurst exponent of fractional Brownian motion. Europhysics Letters, 2009, 86 (3): 30001. doi: 10.1209/0295-5075/86/30001
15	WANG S L, BI D P, RUAN H L Maneuvering target tracking algorithm for cognitive radar based on information entropy criterion. Journal of Electronics, 2019, 47 (6): 1277- 1284.
16	KIM J, WILHELM T What is a complex graph. Physica A: Statistical Mechanics and its Applications, 2008, 387 (11): 2637- 2652. doi: 10.1016/j.physa.2008.01.015

Recognition result	Circular scan	Sector scan	Helical scan	Raster scan	Conical scan	Electromechanical hybrid scan	Two-dimensional electronic scan	Recognition accuracy /%
Circular scan	55	1	2	1	0	0	1	91.7
Sector scan	2	57	0	0	0	1	0	95.0
Helical scan	2	0	54	0	0	3	1	90.0
Raster scan	0	1	1	57	0	1	0	95.0
Conical scan	0	0	1	0	59	0	0	98.3
Electromechanical hybrid scan	0	0	2	1	0	55	2	91.7
Two-dimensional electronic scan	0	0	1	0	0	2	57	95.0

[1]	Luda ZHAO, Bin WANG, Jun HE, Xiaoping JIANG. SE-DEA-SVM evaluation method of ECM operational disposition scheme [J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 600-611.
[2]	Tao WAN, Kaili JIANG, Jingyi LIAO, Yanli TANG, Bin TANG. Detection and recognition of LPI radar signals using visibility graphs [J]. Journal of Systems Engineering and Electronics, 2020, 31(6): 1186-1192.
[3]	Rui Zhang and Min Zhang. SAR target recognition based on active contour without edges [J]. Systems Engineering and Electronics, 2017, 28(2): 276-281.
[4]	Fei Gao, Jingyuan Mei, Jinping Sun, Jun Wang, Erfu Yang, and Amir Hussain. Target detection and recognition in SAR imagery based on KFDA [J]. Journal of Systems Engineering and Electronics, 2015, 26(4): 720-.
[5]	Qing Wu, Leyou Zhang, and Wan Wang. New family of piecewise smooth support vector machine [J]. Systems Engineering and Electronics, 2015, 26(3): 618-625.
[6]	Shaoming Zhang, Fang He, Yunling Zhang, Jianmei Wang, Xiao Mei, and Tiantian Feng. Geometric active contour based approach for segmentation of high-resolution spaceborne SAR images [J]. Journal of Systems Engineering and Electronics, 2015, 26(1): 69-.
[7]	Zhiyu Li, Junfeng Zhang, and Shousong Hu. Incremental support vector machine algorithm based on multi-kernel learning [J]. Journal of Systems Engineering and Electronics, 2011, 22(4): 702-706.
[8]	Sumin Zhang, Shu Li, and Donglin Su. Adaptive support vector machine decision feedback equalizer [J]. Journal of Systems Engineering and Electronics, 2011, 22(3): 452-461.
[9]	Huanhuan Chen, Qiang Wang, and Yi Shen. Decision tree support vector machine based on genetic algorithm for multi-class classification [J]. Journal of Systems Engineering and Electronics, 2011, 22(2): 322-326.
[10]	Shaoming Zhang, Yi Lin, Xiaohu Zhang, and Yingying Chen. Airport automatic detection in large space-borne SAR imagery [J]. Journal of Systems Engineering and Electronics, 2010, 21(3): 390-396.

Automatic radar antenna scan type recognition based on limited penetrable visibility graph

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 14

References 16

Related Articles 10

Recommended Articles

Metrics

Comments