A Class of Multivariate Denoising Algorithms Based on Synchrosqueezing

被引:49
作者
Ahrabian, Alireza [1 ]
Mandic, Danilo P. [1 ]
机构
[1] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, London SW7 2AZ, England
基金
英国工程与自然科学研究理事会;
关键词
Multivariate signal analysis; multivariate signal denoising; short-time Fourier transform; synchrosqueezing transform; wavelet denoising; EMPIRICAL MODE DECOMPOSITION; TIME-FREQUENCY ANALYSIS; HILBERT SPECTRUM; REASSIGNMENT; SIGNALS;
D O I
10.1109/TSP.2015.2404307
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Univariate thresholding techniques based on high resolution time-frequency algorithms, such as the synchrosqueezing transform, have emerged as important tools in removing noise from real world data. Low cost multichannel sensor technology has highlighted the need for direct multivariate denoising, and to this end, we introduce a class of multivariate denoising techniques based on the synchrosqueezing transform. This is achieved by partitioning the time-frequency domain so as to identify a set of modulated oscillations common to the constituent data channels within multivariate data, and by employing a modified universal threshold in order to remove noise components, while retaining signal components of interest. This principle is used to introduce both the wavelet and Fourier based multivariate synchrosqueezing denoising algorithms. The performance of the proposed multivariate denoising algorithm is illustrated on both synthetic and real world data.
引用
收藏
页码:2196 / 2208
页数:13
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