Article Details

Abstract

Wind Turbine Clutter (WTC) severely interferes with meteorological radar monitoring of hazardous weather. To address the problem that the Matrix Completion (MC) method based on Nuclear Norm Minimization (NNM) suffers from insufficient recovery accuracy due to the lack of sharp cutoff in the singular values of the weather signal observation matrix, which makes it difficult to accurately approximate the rank function, a nonconvex matrix completion algorithm using the weighted Schatten-p norm is proposed. First, the range cells contaminated by WTC are zero-filled, and a low-rank Toeplitz matrix is constructed in the Doppler frequency domain, resulting in an observation matrix with randomly distributed zero entries. Second, the weighted Schatten-p norm is employed to approximate the rank function, and a nonconvex matrix completion model is established. Finally, the model is solved using the framework of the Augmented Lagrangian Multiplier (ALM) method and the Alternating Direction Method of Multipliers (ADMM), achieving high-precision recovery of weather signals and effective WTC suppression. Experimental results on real meteorological radar data demonstrate that, compared with the NNM model, the Weighted Schatten-p Norm Minimization (WSNM) model can suppress clutter more effectively and significantly improve the recovery accuracy of weather signals.

Keywords

wind turbine clutter; low-rank Toeplitz matrix; matrix completion; weighted Schatten-p norm; alternating direction method of multipliers

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About the authors

Qiu Cunyin (December 1997-), female, Han ethnicity, from Nanyang, Henan, teaching assistant, master's student, research interests include radar signal processing, digital signal processing and embedded applications.