Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT

doi:10.23919/JSEE.2022.000099

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (5): 1116-1129.doi: 10.23919/JSEE.2022.000099

• Advanced Radar Imaging and Intelligent Processing • Previous Articles Next Articles

Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT

Xiaolong CHEN¹^,*(), Jian GUAN¹(), Jibin ZHENG²(), Yue ZHANG³(), Xiaohan YU⁴()

¹ Naval Aviation University, Yantai 264001, China
² National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
³ School of Electronic and Communication Engineering, Sun Yat-Sen University, Guangzhou 510000, China
⁴ Unit 91977 of the PLA, Beijing 100036, China

Received:2021-09-16 Online:2023-10-18 Published:2023-10-30
Contact: Xiaolong CHEN E-mail:cxlcxl1209@163.com;guanjian_68@163.com;jbzheng@mail.xidian.edu.cn;zhangyue_frog1@163.com;2953164562@qq.com
About author:
CHEN Xiaolong was born in 1985. He received his Ph.D. degree from Naval Aviation University. He is an associated professor in Naval Aviation University. His research interests are radar moving target detection, sea clutter suppression, radar signal refinement processing, etc. E-mail: cxlcxl1209@163.com

GUAN Jian was born in 1968. He is a professor in Naval Aviation University. His research interests are radar target detection and tracking, reconnaissance image processing and information fusion, etc. E-mail: guanjian_68@163.com

ZHENG Jibin was born in 1986. He received his Ph.D. degree from Xidian University. He is an associated professor in Xidian University. His research interests are radar signal refinement processing, swarm intelligence, etc. E-mail: jbzheng@mail.xidian.edu.cn

ZHANG Yue was born in 1980. He received his Ph.D. degree in 2010 in electronic science and technology at the National University of Defense Technology, Changsha, China. His research interests include radar signal processing and automatic targets recognition. E-mail: zhangyue_frog1@163.com

YU Xiaohan was born in 1991. She received her Ph.D. degree from Naval Aviation University. She is an engineer in Unit 91977 of the PLA. Her research interests are radar moving target detection, sparse signal processing, etc. E-mail: 2953164562@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62222120;61871391;U1933135) and Shandong Provincial Natural Science Foundation (ZR2021YQ43).

Abstract

Abstract:

Long-time coherent integration (LTCI) is an effective way for radar maneuvering target detection, but it faces the problem of a large number of search parameters and large amount of calculation. Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter background, and at the same time improving the calculation efficiency has become an urgent problem to be solved. The sparse transformation theory is introduced to LTCI in this paper, and a non-parametric searching sparse LTCI (SLTCI) based maneuvering target detection method is proposed. This method performs time reversal (TR) and second-order Keystone transform (SKT) in the range frequency & slow-time data to complete high-order range walk compensation, and achieves the coherent integration of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform (RSFRFT). It can compensate for the nonlinear range migration caused by high-order motion. S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method, which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.

Key words: radar maneuvering target detection, sea clutter, long-time coherent integration (LTCI), sparse transformation, time reversal (TR), second-order Keystone transform (SKT)

Xiaolong CHEN, Jian GUAN, Jibin ZHENG, Yue ZHANG, Xiaohan YU. Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT[J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1116-1129.

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Method	Integration pulse	Data #1		Data #2		Computing time/ms
Method	Integration pulse	P_clutter	P_Δ	P_clutter	P_Δ	Computing time/ms
MTD	256	0.601 5	0.398 5	0.719 8	0.280 2	8.5
RFT	4 096	0.326 6	0.673 4	0.273 6	0.726 4	254.6
RFRFT	4 096	0.287 1	0.712 9	0.210 3	0.789 7	536.4
TR-SKT-FRFT	4 096	0.773 2	0.226 8	0.208 0	0.792 0	312.2
TR-SKT-RSFRFT	4 096	0.194 3	0.805 7	0.074 2	0.925 8	279.1

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Radar fast long-time coherent integration via TR-SKT and robust sparse FRFT

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