Matrix completion (MC) theory has attracted much attention for its capability of recovering a low-rank matrix through its partial entries. In this paper, we investigate the novel suppression methods of wind turbine clutter (WTC) and introduce the application of MC in WTC suppression for weather radar. First, the vectors of weather signals contaminated by WTC are sequentially constructed into a low-rank snapshot matrix satisfying random undersampling, and then, the weather data can be accurately recovered by minimizing the nuclear norm in the inexact augmented Lagrangian multiplier (IALM) method. The proposed algorithm can effectively suppress not only the wind turbine clutter but also the noise, greatly improving the signal-to-noise ratio of the echo. An experimental test validates the effectiveness of the proposed MC algorithm, and its performance is superior to the widely-used multiquadric interpolation algorithm with potential engineering applications.
2. He, W., X. Wang, and Y. Shi, "Wind turbine clutter mitigation based on matching pursuits," IET International Radar Conference 2015, 1-6, Hangzhou, China, Oct. 2015.
3. Pakrooh, P., A. Homan, and L. L. Scharf, "Multipulse adaptive coherence for detection in wind turbine clutter," IEEE Transactions on Aerospace and Electronic Systems, Vol. 53, No. 6, 3091-3103, 2017.
4. Lok, Y. F., A. Palevsky, and J. Wang, "Simulation of radar signal on wind turbine," IEEE National Radar Conference, 538-543, Arlington, USA, Jun. 2010.
5. Danoon, L. R. and A. K. Brown, "Modeling methodology for computing the radar cross section and doppler signature of wind farms," IEEE Transactions on Antennas & Propagation, Vol. 61, No. 10, 5166-5174, 2013.
6. Evans, J. E., "Ground clutter cancellation for the NEXRAD system,", Lincoln Laboratory: Project Report ATC-122, Oct. 1983.
7. Hubbert, J. C., M. Dixon, and S. M. Ellis, "Weather radar ground clutter. Part I: Identification, modeling, and simulation," Journal of Atmospheric & Oceanic Technology, Vol. 26, No. 7, 1165-1180, 2009.
8. Hubbert, J. C., M. Dixon, and S. M. Ellis, "Weather radar ground clutter. Part II: Real-time identification and filtering," Journal of Atmospheric & Oceanic Technology, Vol. 26, No. 7, 1181-1197, 2009.
9. Uysal, F., "Signal processing techniques forwind turbine clutter mitigation,", New York University, New York, 2016.
10. Candes, E. J. and T. Tao, "The power of convex relaxation: Near-optimal matrix completion," IEEE Transactions on Information Theory, Vol. 56, No. 5, 2053-2080, 2010.
11. Deng, B., R. Tao, and D. F. Ping, "Moving-target-detection algorithm with compensation for Doppler migration based on FRFT," Binggong Xuebao/Acta Armamentarii, Vol. 20, No. 10, 1303-1309, 2011.
12. Candes, E. J. and Y. Plan, "Matrix completion with noise," Proceedings of the IEEE, Vol. 98, No. 6, 925-936, 2010.
13. He, W. K. and Q. P. Zai, "Wind turbine radar clutter detection method based on Micro-Doppler characteristics of wind turbine," Journal of Signal Processing, Vol. 33, No. 4, 1-9, 2017.
14. Yang, D., G. S. Liao, and S. Q. Zhu, "Improved low-rank recovery method for sparsely sampling data in array signal processing," Journal of Xidian University, Vol. 41, No. 5, 30-35, 2014.
15. Suleiman, W. and M. Pesavento, "Performance analysis of the decentralized eigendecomposition and ESPRIT algorithm," IEEE Transactions on Signal Processing, Vol. 64, No. 9, 2375-2386, 2015.
16. Mohammad-Hossein, G. H., G. Zhang, and Y. Li, "Detection of ground clutter from weather radar using a dual-polarization and dual-scan method," Atmosphere, Vol. 7, No. 6, 83-93, 2016.
17. Ai, W., et al., "Ground clutter removing for wind profiler radar signal using adaptive wavelet threshold," International Conference on Measuring Technology & Mechatronics Automation IEEE Computer Society, 370-373, Washington, USA, Mar. 2010.
18. Yang, J. F., X. M. Yuan, and , "Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization," Mathematics of Computation, Vol. 82, No. 281, 301-329, 2011.
19. Li, M., Z. He, and W. Li, "Transient interference mitigation via supervised matrix completion," IEEE Geoscience & Remote Sensing Papers, Vol. 13, No. 7, 907-911, 2016.
20. Turso, S. and T. Bertuch, "Electronically steered cognitive weather radar — A technology perspective," The 2017 IEEE Radar Conference, Seattle, Washington, USA, May 2017, DOI: 10.1109/RADAR.2017.7944266.
21. Koredianto, U. and R. Mohammad, "Comparison of classical interpolation methods and compressive sensing for missing data reconstruction," 2019 IEEE International Conference on Signals and Systems (ICSigSys), Bandung, Indonesia, Jul. 2019.
22. Li, B. and A. P. Petropulu, "Optimum co-design for spectrum sharing between matrix completion based MIMO radars and a MIMO communication system," IEEE Transactions on Signal Processing, Vol. 64, No. 17, 4562-4575, 2016.