正交变分模态分解

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing Pub Date : 2025-08-22 DOI:10.1016/j.sigpro.2025.110251

Himpu Marbona , Daniel Rodríguez , Alejandro Martínez-Cava , Eusebio Valero

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引用次数: 0

摘要

本文通过提高最小化问题中的模态正交性，对变分模态分解（VMD）进行了改进。其主要思想是通过施加弱正交条件来主动发送和接收模式之间的非正交信号分量。该方法与滤波器带宽的比例值相结合，有效地防止了模式重复，增强了分解对过分割的鲁棒性。在宽带合成信号下的实验表明，与标准变分模态分解（VMD）相比，该方法的性能有所提高。本文还研究了该方法对不同滤波器带宽、噪声水平的敏感性及其处理过分割的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Orthogonalized variational mode decomposition

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Orthogonalized variational mode decomposition

This paper introduces a modification to variational mode decomposition (VMD) by promoting mode orthogonality in the minimization problem. The main idea is to actively transmit and receive non-orthogonal signal components between the modes by imposing weak orthogonality conditions. The approach, combined with the proportional value of filter bandwidth, effectively prevents mode duplication and enhances robustness of the decomposition against over-segmentation. Experiments considering a broadband synthetic signal show the improved performance of this method in comparison to the standard Variational Mode Decomposition (VMD). The sensitivity of the method to different filter bandwidth, levels of noise, and its effectiveness in handling over-segmentation are also examined.

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来源期刊

Signal Processing 工程技术-工程：电子与电气

CiteScore

9.20

自引率

9.10%

发文量

309

审稿时长

41 days

期刊介绍： Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.