基于模糊递归和最优硬阈值的局部投影降噪算法

doi:10.12305/j.issn.1001-506X.2023.03.01

Abstract

Abstract:

For the local neighborhood selection problem and subspace partition problem of the original local projective (LP) noise reduction algorithm, an improved LP noise reduction algorithm based on fuzzy recurrence plot and optimal hard threshold criterion is proposed. Firstly, the local neighborhood is determined by fuzzy recurrence plot. Then, the singular value decomposition (SVD) is implemented to local neighborhood matrix, and signal subspace and noise subspace in local neighborhood is partitioned by optimal hard threshold criterion. Finally, the projection for denoising is performed. The denoising results of Lorenz signal show that the proposed algorithm can improve the signal to noise ratio, reduce the mean square error, and recovery the morphological structure of the original attractor. After processing the measured noisy electrocardiogram (ECG) signal, the signal to noise ratio is significantly improved, which shows the effectiveness of the proposed algorithm.

Key words: local projection, fuzzy recurrence plot, optimal hard threshold, noise reduction, chaotic signal

CLC Number:

TN911.7

Dong WANG, Zhongma CUI, Wendong CHEN, Qin SHU. Local projective noise reduction algorithm based on fuzzy recurrence and optimal hard threshold[J]. Systems Engineering and Electronics, 2023, 45(3): 621-628.

Figures/Tables 8

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References 31

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方法	李雅普诺夫指数	关联维数	耗时/s
原始信号	1.657 96	2.065 44	-
本文方法	1.722 05	2.135 53	10.823
标准局部投影	1.443 18	2.511 27	4.878
递归局部投影	1.564 47	2.235 61	6.453
基于小波的局部投影	1.544 73	2.215 46	8.613
基于EMD的局部投影	1.556 42	2.226 54	8.456
K均值局部投影	1.577 46	2.151 46	9.862
平滑子空间局部投影	1.486 25	2.496 54	5.061

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Local projective noise reduction algorithm based on fuzzy recurrence and optimal hard threshold

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