Bayesian track-before-detect algorithm for nonstationary sea clutter

doi:10.23919/JSEE.2021.000113

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1338-1344.doi: 10.23919/JSEE.2021.000113

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Bayesian track-before-detect algorithm for nonstationary sea clutter

Cong XU^1,*(), Zishu HE¹(), Haicheng LIU²(), Yadan LI³()

¹ Department of Information and Communication, University of Electronic Science and Technology of China, Chengdu 610051, China
² Department of Electrical and Information Engineering, Heilongjiang Institute of Engineering, Harbin 150026, China
³ College of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-10-28 Accepted:2021-11-30 Online:2022-01-05 Published:2022-01-05
Contact: Cong XU E-mail:xucong_0803@126.com;zshe@uestc.edu.cn;liuhaicheng@126.com;Liyadan@163.com
About author:|XU Cong was born in 1983. She received her B.S. and Ph.D. degrees in communication and information system from Harbin Engineering University, Harbin, in 2006, and 2010, respectively. She is now engaged in post doctoral work in signal and information processing with the Department of Information and Communication, University of Electronic Science and Technology of China. Her research interests are radar weak signal detection and radar target tracking. E-mail: xucong_0803@126.com||HE Zishu was born in 1962. He received his B.S., M.S., and Ph.D. degrees in signal and information processing from University of Electronic Science and Technology of China (UESTC), Chengdu, in 1984, 1988, and 2000, respectively. He is a professor in signal and information processing with the Department of Information and Communication, UESTC. His research interests involve array signal processing, digital beamforming, the theory on MIMO communication and MIMO radar, adaptive signal processing, and channel estimation. He has published more than 100 articles and two books on signals and systems and modern digital signal processing and its applications. E-mail: zshe@uestc.edu.cn||LIU Haicheng was born in 1979. He received his B.S degree in Electronic Information Engineering from Northeast Agricultural University, Harbin, in 2003 and M.S degree in Electronic and Communication Engineering from Harbin Institute of Technology, Harbin, in 2012. He is now working with the Depatment of Electrical and Information Engineering in Heilongjiang Institute of Engineering. His research interests are signal processing and intelligent hardware.E-mail: liuhaicheng@126.com||LI Yadan was born in 1985. She received her double B.S. degrees in business administration and radar communication from Civil Aviation University of China in 2008. She received her M.S. degree in business administration from University of Chinese Academy of Sciences in 2020. She is now engaged in air traffic controll in Air Traffic Management Bureau of China Civilaviation Administration. Her research interests are radar signal detection, communication navigation, and air traffic control system construction. E-mail: Liyadan@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61671139)

Abstract

Abstract:

Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter. The track-before-detect (TBD) filter is an effective way to increase the signal-to-clutter ratio (SCR), thus improving the detection performance of small targets in sea clutter. To cope with the nonstationary characteristic of sea clutter, an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process. The detection threshold is set according to the parameter estimation result under the framework of information theory. For detection of closely spaced targets, those within the same range cell as the one under test are treated as contribution to sea clutter, and a successive elimination method is adopted to detect them. Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter, especially closely spaced ones.

Key words: small target, track-before-detect (TBD), nonstationary sea clutter, closely spaced target

Cong XU, Zishu HE, Haicheng LIU, Yadan LI. Bayesian track-before-detect algorithm for nonstationary sea clutter[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1338-1344.

Figures/Tables 10

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References 23

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Parameter		Value
T_X frequency		X-band
Range resolution/m		6
Pulse repetition/kHz		3
Range distance/m		3577.5–4572.5 (about 166 gates)
Duration/s		2

Target	Velocity/(m·s^?1)	Location/m	Emergence time/s	SCR/dB
Target 1	100	3677.5	0.5	2
Target 2	100	4077.5	1	2
Target 3	100	4277.5	1.5	2

Target	Velocity/(m·s^?1)	Location/m	Emergence time/s	SCR/dB
Target 1	100	3677.5	0.5	2
Target 2	100	3677.5	0.5	2
Target 3	100	3677.5	0.5	2

[1]	Zhichao BAO, Qiuxi JIANG, Fangzheng LIU. Multiple model efficient particle filter based track-before-detect for maneuvering weak targets [J]. Journal of Systems Engineering and Electronics, 2020, 31(4): 647-656.
[2]	Liangkui LIN, Shaoyou WANG, Zhongxing TANG. Using deep learning to detect small targets in infrared oversampling images [J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 947-952.
[3]	Xiujie Qu, He Chen, and Guihua Peng. Novel detection method for infrared small targets using weighted information entropy [J]. Journal of Systems Engineering and Electronics, 2012, 23(6): 838-842.

Bayesian track-before-detect algorithm for nonstationary sea clutter

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