Detection and recognition of LPI radar signals using visibility graphs

doi:10.23919/JSEE.2020.000091

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (6): 1186-1192.doi: 10.23919/JSEE.2020.000091

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Detection and recognition of LPI radar signals using visibility graphs

Tao WAN*(), Kaili JIANG(), Jingyi LIAO(), Yanli TANG(), Bin TANG()

¹ School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received:2020-04-13 Online:2020-12-29 Published:2020-12-29
Contact: Tao WAN E-mail:taowan.uestc0939@foxmail.com;jiangkelly@foxmail.com;LiaoJingyi@std.uestc.edu.cn;aaa123@yahoo.cn;bint@uestc.edu.cn
About author:|WAN Tao was born in 1995. He received his B.S. degree from Harbin University of Commerce in 2017, Harbin, China. He is currently pursuing his Ph.D. degree with the University of Electronic Science and Technology of China. He is interested in electronic reconnaissance and is mainly engaged in electronic countermeasures, signal processing, and machine learning. E-mail: taowan.uestc0939@foxmail.com||JIANG Kaili was born in 1991. She received her B.S. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2013. Currently, she is working toward her Ph.D. degree in the School of Information and Communication Engineering. Her research interests include wideband spectrum sensing, sparse/compressive sensing and radar signal processing. E-mail: jiangkelly@foxmail.com||LIAO Jingyi was born in 1996. She received her B.S. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2018. She is currently pursuing her M.S. degree with the School of Information and Communication Engineering. Her research interest includes LPI radar signal detection and classification. E-mail: LiaoJingyi@std.uestc.edu.cn||TANG Yanli was born in 1987. She obtained her bachelor’s degree and master’s degree in Chengdu University of Information Technology in 2010 and 2015, respectively. Currently she is pursuing her doctorate degree in the University of Electronic Science and Technology of China. Her major is information and communication engineering. Her research interests include radar countermeasure networks and radar signal reconnaissance. E-mail: aaa123@yahoo.cn||TANG Bin was born in 1963. He is a professor of the University of Electronic Science and Technology of China. His research interests include complex/complex modulation LPI and new system radar reconnaissance and interference technology, adaptive radar reconnaissance and interference technology, networked radar countermeasure technology, broadband/ultra-wideband radar digital reconnaissance receiving and interference technology. E-mail: bint@uestc.edu.cn
Supported by:
This work was supported by the National Defence Pre-research Foundation of China (30502010103)

Abstract

Abstract:

The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare (EW). So far, however, there are still problems with signal detection and recognition, especially in the low probability of intercept (LPI) radar. This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs (VG). More network and feature information can be extracted in the VG two-dimensional space, this algorithm can solve the problem of signal recognition using the autocorrelation function. Wavelet denoising processing is introduced into the signal to be tested, and the denoised signal is converted to the VG domain. Then, the signal detection is performed by using the constant false alarm of the VG average degree. Next, weight the converted graph. Finally, perform feature extraction on the weighted image, and use the feature to complete the recognition. It is testified that the proposed algorithm offers significant improvements, such as robustness to noise, and the detection and recognition accuracy, over the recent researches.

Key words: detection, recognition, visibility graph (VG), support vector machine (SVM), k-nearest neighbor (KNN)

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.

Figures/Tables 5

Table 1

Table 2

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Table 3

References 34

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Modulation type	${f_c}[k]$ / ${\rm{Hz}}$	$\varphi [k]$ / ${\rm{rad}}$
MP	constant	constant
LFM	${f_0} + \dfrac{B}{{{\tau _{pw}}}}(k{T_s})$	constant
BPSK	constant	(0, ${\text{π}} $ )
QPSK	constant	$\left(0,\dfrac{ {\text{π} } }{2}, {\text{π} }, \dfrac{ {\text{3π} } }{2} \right)$
FSK	$\{ {f_1},{f_2}, \cdots ,{f_{ {N_F} } }\}\}$	constant
V-Shaped	$\left\{ \begin{array}{l} {f_0} + \dfrac{B}{ { {\tau _{pw} } } }(k{T_s}),\;\;0 \leqslant k < \dfrac{ { {\tau _{pw} } } }{2} \\ {f_0} + \dfrac{B}{2} - \dfrac{B}{ { {\tau _{pw} } } }(k{T_s}),\;\;\dfrac{ { {\tau _{pw} } } }{2} \leqslant k <{\tau _{pw} } \end{array} \right.$	constant
LFM-BPSK	${f_0} + \dfrac{B}{{{\tau _{pw}}}}(k{T_s})$	(0, ${\text{π}} $ )
LFM-QPSK	${f_0} + \dfrac{B}{{{\tau _{pw}}}}(k{T_s})$	$\left(0,\dfrac{ {\text{π} } }{2}, {\text{π} }, \dfrac{ {\text{3π} } }{2} \right)$
Barker	constant	(+1,+j,?1,+j,+1)
Frank	constant	$\dfrac{{2{\text{π}} }}{M}(i - 1)(j - 1)$
P1	constant	$ - \dfrac{{\text{π}} }{M}[M - (2j - 1)][(j - 1)M + (i - 1)]$
P2	constant	$ - \dfrac{{\text{π}} }{{2M}}[2i - 1 - M][2j - 1 - M]$
P3	constant	$\dfrac{ - {\text{π} } {(i - 1)^2} }{ {N_c} }$
P4	constant	$\dfrac{{\text{π} } {(i - 1)^2}}{{N_c}} - {\text{π} } (i - 1)$

Motif	Criterion
Motif	M0	M1
VG	$\{ \forall {y_0},{y_1}:{y_2} \leqslant 2{y_1} - {y_0}\} $	$\{ \forall {y_0},{y_1}:{y_2} > 2{y_1} - {y_0}\} $
HVG	$\{ \forall {y_0},{y_2}:{y_1} > {y_0}\} \cup \{ \forall {y_0}:{y_1} < {y_0},{y_2} < {y_1}\} $	$\{ \forall {y_0}:{y_1} < {y_0},{y_2} > {y_1}\} $

Source	Dataset	Method	Overall SRP
Reference [30]	9 kinds of LPI radar waveforms	Time-frequency analysis+ multilayer perceptron (MLP)	0.765
Reference [8]	9 kinds of LPI radar waveforms	Time-frequency analysis + Elman neural network (ENN)	0.938
Reference [31]	12 kinds of LPI radar waveforms	Spectral correlation+ SVM	0.887
This paper	14 kinds of LPI radar waveforms	VG+SVM+KNN	0.974

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Detection and recognition of LPI radar signals using visibility graphs

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