变分模式分解方法研究与应用综述

自适应信号分解领域一个十分活跃的分支——变分模式分解(VMD)已经成为信号处理与检测技术学界一个热门的研究方向。VMD对非平稳、非线性信号具有良好的处理效果。针对VMD模型及其参数选择，发展了许多拓展模型及参数优化方法。本文回顾近十年来VMD的研究进展，对相关的文献进行总结与分析。首先，分析VMD的原理性优势及其在各个领域的应用潜力；其次，根据模型对不同信号类型的匹配能力，分类总结VMD拓展模型的不同特性和适用场景；然后，归纳VMD及其拓展模型参数优化方法的研究现状，探讨与分析不同模型参数优化方法的特点和最新研究趋势；最后，对VMD的未来发展提出6点展望，为后续的研究指明方向。

Review on research and application of variational mode decomposition

Variational mode decomposition (VMD), a very active branch in the field of adaptive signal decomposition, has become a hot research direction in the field of signal processing. VMD shows good performance in processing non-stationary and nonlinear signals. Aiming at VMD model and its parameter selection, many extended models and parameter optimization methods have been studied. This article reviews the research progress of VMD in recent decade, summarizes and analyzes the relevant literature. Firstly, it analyzes the principle advantages of VMD and its application potential in various fields. Secondly, according to the matching ability of the model to different signal types, the different characteristics and applicable scenarios of extended models of VMD are summarized by classification. Then, the research progress of parameter optimization methods for VMD and its extended models are summarized to discuss and analyze the characteristics of different model parameter optimization methods and the latest research trends. Finally, 6 prospects are put forward for the future development of VMD, pointing out the direction for subsequent research.