ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

doi:10.23919/JSEE.2020.000090

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (6): 1178-1185.doi: 10.23919/JSEE.2020.000090

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

Hongyin SHI*(), Yue LIU(), Jianwen GUO(), Mingxin LIU()

¹ School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Received:2019-08-20 Online:2020-12-18 Published:2020-12-29
Contact: Hongyin SHI E-mail:shihy@ysu.edu.cn;mlle_liuyue@163.com;jianwen_guo@yeah.net;liumx@ysu.edu.cn
About author:|SHI Hongyin was born in 1976. He received his Ph.D. degree from Beihang University in 2009. Now, he is a professor in Yanshan University. His main research interests include SAR imaging and moving target detection. E-mail: shihy@ysu.edu.cn||LIU Yue was born in 1995. She received her B.S. degree from Yanshan University in 2017. Now, she is a master student in School of Information Science and Engineering, Yanshan University. She is mainly engaged in the research of deep learning and ISAR imaging. E-mail: mlle_liuyue@163.com||GUO Jianwen was born in 1993. He received his B.S. degree from Yanshan University in 2017. Now, he is a master student in School of Information Science and Engineering, Yanshan University. he is mainly engaged in the research of deep learning and ISAR imaging. E-mail: jianwen_guo@yeah.net||LIU Mingxin was born in 1976. He received his Ph.D. degree from Yanshan University in 2006. He is currently a professor with School of Information Science and Engineering, Yanshan University. His current research interests include stochastic modeling for wireless communication networks and performance valuation for communication system. E-mail: liumx@ysu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61571388; 61871465; 62071414) and the Project of Introducing Overseas Students in Hebei Province (C20200367)

Abstract

Abstract:

The issue of small-angle maneuvering targets inverse synthetic aperture radar (ISAR) imaging has been successfully addressed by popular motion compensation algorithms. However, when the target’s rotational velocity is sufficiently high during the dwell time of the radar, such compensation algorithms cannot obtain a high quality image. This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm. The keystone transform is used to coarsely compensate for the target’s rotational motion and translational motion, and the deep learning algorithm is used to achieve a super-resolution image. The uniformly distributed point target data are used as the data set of the training u-net network. In addition, this method does not require estimating the motion parameters of the target, which simplifies the algorithm steps. Finally, several experiments are performed to demonstrate the effectiveness of the proposed algorithm.

Key words: inverse synthetic aperture radar (ISAR), maneuvering target, keystone transform, deep learning, u-net network

Hongyin SHI, Yue LIU, Jianwen GUO, Mingxin LIU. ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform[J]. Journal of Systems Engineering and Electronics, 2020, 31(6): 1178-1185.

Figures/Tables 13

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Radar parameter name	Symbol	Numerical value
Initial frequency of transmitted signal	${f_0}$ /GHz	3
Target initial distance	${R_0}$ /km	10
Pulse repetition frequency	$PRF$ /KHz	50
Transmit signal bandwidth	$B$ /MHz	150
Number of bursts	${M_{{\rm{burst}}} }$	128
Number of pulses	${N_{{\rm{pulse}}} }$	128

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ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

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