An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

doi:10.23919/JSEE.2020.000046

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (4): 712-721.doi: 10.23919/JSEE.2020.000046

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An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

Wen JIANG(), Xiongjun FU*(), Jiayun CHANG(), Rui QIN()

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Received:2019-08-27 Online:2020-08-25 Published:2020-08-25
Contact: Xiongjun FU E-mail:jwen912@126.com;fuxiongjun@bit.edu.cn;824400828@qq.com;qinrui90@163.com
About author:JIANG Wen was born in 1991. He received his master's degree from Zhengzhou University, China, in 2016. Currently, He is pursuing his Ph.D. degree in the School of Information and Electronics, Beijing Institute of Technology (BIT). His research interests include radar signal processing and radar pulses deinterleaving. E-mail: jwen912@126.com|FU Xiongjun was born in 1978. He received hisB.E. and Ph.D. degrees from Beijing Institute of Technology (BIT), China, in 2000 and 2005 respectively. He is currently the vice deanof the School of Information and Electronics, BIT, and an associateprofessor and Ph.D. supervisor with BIT. His current researchinterests include radar system, radar signal processing, waveformdesign and automatic target recognition. E-mail: fuxiongjun@bit.edu.cn|CHANG Jiayun was born in 1989. She received her master's degree from Beijing Institute of Technology (BIT), China, in 2016. Currently, she is pursuing her Ph.D. degree in the School of Information and Electronics, BIT. Her research interests include automatic target recognition and radar signal processing. E-mail: 824400828@qq.com|QIN Rui was born in 1990. He received his master's degree from Wuhan University of Technology, China, in 2015. Currently, he is pursuing his Ph.D. degree in the School of Information and Electronics, Beijing Institute of Technology (BIT). His research interests include radar signal processing, SAR image processing and pattern recognition. E-mail: qinrui90@163.com
Supported by:
the National Natural Science Foundation of China(61571043);the 111 Project of China(B14010);This work was supported by the National Natural Science Foundation of China (61571043) and the 111 Project of China (B14010)

Abstract

Abstract:

As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map (SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology (SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then, structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process, constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

Key words: de-interleaving, self-organizing feature map (SOFM), self-adaptive network topology (SANT)

Wen JIANG, Xiongjun FU, Jiayun CHANG, Rui QIN. An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology[J]. Journal of Systems Engineering and Electronics, 2020, 31(4): 712-721.

Figures/Tables 12

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References 24

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Mode	Radar	AOA/(°)	PW/$\mu $s	RF/MHz	Pulse loss/%	PRI
Multi-mode	1	29.5-31.5	9.5-10.5	2.28-2.52	10	Constant: 400 $\mu$s
	2	29.5-31.5	9.5-10.5	3.32-3.68	10	Constant: 400 $\mu$s
	3	29.5-31.5	9.5-10.5	4.28-4.73	10	Jitter: 10%; Center: 400 $\mu$s
	4	29.5-31.5	9.5-10.5	5.7-6.3	10	Jitter: 30%; Center: 400 $\mu$s
Single-mode	5	38-42	9.5-10.5	7.6-8.4	5	Jitter: 20%; Center: 100 $\mu$s
	6	47.5-52.5	19-21	4.75-5.25	5	Hopping: 10%; Center: 190 $\mu$s
	7	57-63	4.75-5.25	9.5-10.5	5	Grops: 300/330/370/420 $\mu$s

Algorithm	Computational complexity
SOFM	$N_1 \times C_K + C_I$
SANT-SOFM	$N_2 \times C_K + C_I + C_P + C_O$

An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology

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