Generalized Analytical Mode Decomposition for Non-Stationary Signal Analysis

doi:10.3969/j.issn.0372-2112.2016.06.029

您当前的位置：

首页 >

文章列表页 >

Generalized Analytical Mode Decomposition for Non-Stationary Signal Analysis

更新时间：2025-07-02

- Generalized Analytical Mode Decomposition for Non-Stationary Signal Analysis
- Acta Electronica Sinica Vol. 44, Issue 6, Pages: 1458-1464(2016)
- 作者机构：
  
  1. 安徽工业大学机械工程学院,安徽,马鞍山,243032
  2. 湖南大学汽车车身先进设计制造国家重点实验室,湖南,长沙,410082
  3. 安徽工业大学机械工程学院,安徽,马鞍山,243032
  4. 湖南大学汽车车身先进设计制造国家重点实验室,湖南,长沙,410082
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51375152, No.51505002);Program of Natural Science Foundation for Colleges and Universities in Anhui Province (No.KJ2015A080)
- DOI：10.3969/j.issn.0372-2112.2016.06.029
  CLC： TN911.7
- Published：2016
- 稿件说明：
移动端阅览
Generalized Analytical Mode Decomposition for Non-Stationary Signal Analysis[J]. Acta Electronica Sinica, 2016, 44(6): 1458-1464.
DOI：

Generalized Analytical Mode Decomposition for Non-Stationary Signal Analysis[J]. Acta Electronica Sinica, 2016, 44(6): 1458-1464. DOI： 10.3969/j.issn.0372-2112.2016.06.029.

摘要

现有的非平稳信号分析方法都有各自不同的缺陷

短时傅里叶变换的时频分辨率受不确定性原理的限制

希尔伯特黄变换存在端点效应和模态混叠

易导致模糊的时频分布;解析模态分解只适合分析频率恒定的多分量信号;针对包含多个时变模态、特别是频谱重叠的非平稳信号

本文提出了一种新的信号分析方法广义解析模态分解(Generalized Analytical Mode Decomposition

GAMD).GAMD通过广义傅里叶变换将时变频率转换为频谱可分的

采用解析模态分解对其分解

再对得到的单分量信号进行逆广义傅里叶变换即可得到原始信号的分量.因此

GAMD非常适合分析时变的非平稳信号.通过仿真信号将GAMD与短时傅里叶变换和希尔伯特黄变换等方法进行了对比

结果表明GAMD方法的分解效果更精确

时频分辨率更高.

Abstract

The existing methods for analyzing non-stationary signal all have different defects.The time-frequency resolution of short-time Fourier transform is limited by the uncertainty principle.The boundary effect and mode mixing of Hilbert-Huang transform often result in an unclear time-frequency distribution.Analytical mode decomposition is only suitable for analyzing multi-component signal with constant frequencies.For the multi-component signal with time-varying frequencies

especially when the spectrum overlaps

a method termed generalized analytical mode decomposition (GAMD) is proposed for analyzing these signals.In GAMD

generalized Fourier transform is used to convert the time-varying frequencies to constant ones without overlapping in spectrum and then the analytical mode decomposition is adopted to handle the transformed signal.Lastly

the inverse generalized Fourier transform is used to demodulate the obtained mono-components.Hence

GAMD is very suitable for analyzing time-varying non-stationary signals.The proposed GAMD method is compared with short-time Fourier transform and Hilbert-Huang transform through analyzing simulation signals and the results indicate that GAMD possesses more accurate decomposing results and higher time-frequency resolution.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Sparse Time-Varying Modeling and Time-Frequency Representation of EEG Data Using Scale-Adaptive Multiwavelet

Radar Signal Modulation Recognition Based on Split EfficientNet Under Low Signal-to-Noise Ratio

A Space-Frequency Block Code Recognition Based on Deep Multilevel Residual Network with Low SNR

Local Mean Computation for Empirical Mode Decomposition Based on Higher Order Derivative

Related Author

ZHENG Nan

LI Yu-rong

SHI Wu-xiang

TAN Ji-yu

CHEN Wen-sheng

LI Qin

LIU Wei

NIU Chao-yang

Related Institution

Fujian Key Laboratory of Medical Instrument and Pharmaceutical Technology

College of Electrical Engineering and Automation, Fuzhou University

PLA Strategic Support Force Information Engineering University

Department of Information Fusion, Naval Astronautical University

School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin Guangxi China

⁰