论文标题
基于通用多元降解波动分析的多元信号denoising
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis
论文作者
论文摘要
我们提出了一种新型的多元信号去核方法，该方法通过考虑数据的固有通道间依赖性来对输入数据中多种模式进行远程相关分析。这是通过新颖而通用的多元变化分析（DFA）方法来实现的 - 本文的另一个贡献。具体而言，我们提出的denoising方法首先使用多元变分模式分解（MVMD）方法获得数据驱动的多尺度信号表示。然后，所提出的通用多元DFA用于基于其随机度得分拒绝嘈杂的模式。最后，通过使用主成分分析（PCA）去除噪声轨迹后的剩余模式，通过将其剩余模式求和来重建。
We propose a novel multivariate signal denoising method that performs long-range correlation analysis of multiple modes in input data by considering inherent inter-channel dependencies of the data. That is achieved through a novel and generic multivariate extension of detrended fluctuation analysis (DFA) method - another contribution of this paper. Specifically, our proposed denoising method first obtains data driven multiscale signal representation using multivariate variational mode decomposition (MVMD) method. Then, the proposed generic multivariate DFA is used to reject the predominantly noisy modes based on their randomness scores. Finally, the denoised signal is reconstructed by summing the remaining modes albeit after the removal of the noise traces using the principal component analysis (PCA).
