A Truncated Matrix Completion Algorithm Using Prior Information for Wind Turbine Clutter Suppression

Minghe Mao,Mingwei Shen,Tianhe Li,Rui Shi,Rui Li,Ning Cao,Sébastien Poncet

doi:10.1155/2021/8814415

Minghe Mao, Mingwei Shen + Show 5 more

Open Access

https://doi.org/10.1155/2021/8814415

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Abstract

The conventional matrix completion (MC) regularizes each singular value equally, and thus the rank cannot be well approximated, which greatly limits the flexibility and accuracy of MC usage. In this paper, a truncated MC algorithm using prior information to determine the threshold while generating the target rank is proposed for the wind turbine clutter suppression of weather radar. During the singular value shrinking process, an appropriate threshold is selected to obtain the optimal approximation of the sampling matrix. Specifically, the mean value of the diagonal element in the recovered weather matrix is calculated to improve the robustness of the recovery result effectively. Simulation results demonstrate that the proposed algorithm reduces the computational complexity as well as further improves the MC accuracy and realizes the effective suppression of the wind turbine clutter.

Highlights

In response to the global energy crisis and climate change, countries around the world have a huge demand for renewable and clean energy
When the number of singular values increases to r, the reconstructed curve is getting closer to the original curve
In the process of singular value contraction, the part of unimportant noise is ignored, and the largest part of the singular value related to the main component of the matrix is not contracted, which better protects the effective components of the matrix

Summary

Introduction

In response to the global energy crisis and climate change, countries around the world have a huge demand for renewable and clean energy. When the singular values of the nuclear norm are thresholded with the same constant, the information of large singular values will be lost [20], and the recovered data will generate a low peak signal-tonoise ratio (SNR), which greatly limits its ability and flexibility to deal with many practical problems such as denoising. Erefore, this paper fully integrates the prior information in the matrix to obtain prior knowledge about the weights of different singular values according to the observation matrix, introduces a truncated nuclear norm in the objective function, and proposes a truncated matrix completion (TMC) algorithm [21, 22] using prior information.

Signal Model

Low-Rank Minimization with Truncated Nuclear Norm

TMC Algorithm Design Based on Mean Value Technique

Simulation Results

Conclusion