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.
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