Wind turbine clutter mitigation using morphological component analysis with group sparsity

doi:10.23919/JSEE.2022.000157

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (3): 714-722.doi: 10.23919/JSEE.2022.000157

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles

Wind turbine clutter mitigation using morphological component analysis with group sparsity

Xiaoyu WAN¹, Mingwei SHEN¹^,*(), Di WU²(), Daiyin ZHU²()

¹ College of Computer and Information, Hohai University, Nanjing 211100, China
² Key Laboratory of Radar Imaging and Microwave Photonics & Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2021-05-21 Accepted:2022-06-14 Online:2023-06-15 Published:2023-06-30
Contact: Mingwei SHEN E-mail:smw_hhu1981@163.com;wudi82@nuaa.edu.cn;zhudy@nuaa.edu.cn
About author:
WAN Xiaoyu was born in 1996. She received her B.S. degree in electronic and information engineering from Xinyang Normal University, Xinyang, Henan, in 2018. She is a M.S. candidate of Hohai University. Her research interest is radar signal processing. E-mail: wxy_960@163.com

SHEN Mingwei was born in 1981. He received his B.S. and Ph.D. degrees in electronic engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, in 2003 and 2008, respectively. Since 2011, he has been with the College of Computer and Information, Hohai University, where he is currently a professor. His research interests include space-time adaptive processing and syntheic aperture radar ground moving target indication. E-mail: smw_hhu1981@163.com

WU Di was born in 1982. He received his Ph.D. degree in communication and information systems from Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2011. In 2011, he joined the College of Electronic and Information Engineering and the Key Laboratory of Radar Imaging and Microwave Photonics & Ministry of Education, NUAA, where he is currently an associate professor. His research interests include radar imaging algorithms, space-time adaptive processing, and SAR ground moving target indication. E-mail: wudi82@nuaa.edu.cn

ZHU Daiyin was born in 1974. He received his Ph.D. degree in electronics from Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, in 2002. From 1998 to 1999, he was a guest scientist with the Institute of Radio Frequency Technology, German Aerospace Center, Oberphaffenhofen, where he was involved in SAR interferometry. In 1998, he joined the Department of Electronic Engineering, NUAA, where he is currently a professor. He has developed algorithms for several operational airborne SAR systems. His research interests include radar imaging algorithms, SAR/inverse SAR autofocus techniques, SAR ground moving target indication, and SAR interferometry. E-mail: zhudy@nuaa.edu.cn

Abstract

Abstract:

To address the problem that dynamic wind turbine clutter (WTC) significantly degrades the performance of weather radar, a WTC mitigation algorithm using morphological component analysis (MCA) with group sparsity is studied in this paper. The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo. After that, the MCA algorithm is applied and the window used in the short-time Fourier transform (STFT) is optimized to lessen the spectrum leakage of WTC. Finally, the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution, thus contributing to better estimation performance of weather signals. The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

Key words: weather radar, wind turbine clutter (WTC), morphological component analysis (MCA), short-time Fourier transform (STFT), group sparsity

Xiaoyu WAN, Mingwei SHEN, Di WU, Daiyin ZHU. Wind turbine clutter mitigation using morphological component analysis with group sparsity[J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 714-722.

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Parameter	Value
Pulse repetition frequency/Hz	$ 3 \times {10^3} $
Pulse number	$ 1.5 \times {10^4} $
Distance between radar and turbine nacelle/m	707
Length of blade/m	60
Angular velocity of blade/(r $ \cdot $ min⁻¹)	12
Elevation angle/(°)	45
SNR of a single pulse/dB	0−30

Wind turbine clutter mitigation using morphological component analysis with group sparsity

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