Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Time-frequency analysis method based on nonlinear mode decomposition and adaptive optimal kernel

A technology of mode decomposition and time-frequency analysis, applied in the field of time-frequency analysis of non-stationary signals, can solve the problems of noise sensitivity and insufficiency

Inactive Publication Date: 2017-05-10
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF5 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] EMD+AOK can reduce the influence of cross terms when analyzing multi-component signals, but it is too sensitive to noise
EEMD+AOK can improve the anti-noise performance to a certain extent, but it is not enough

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Time-frequency analysis method based on nonlinear mode decomposition and adaptive optimal kernel
  • Time-frequency analysis method based on nonlinear mode decomposition and adaptive optimal kernel
  • Time-frequency analysis method based on nonlinear mode decomposition and adaptive optimal kernel

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0085] In the following, a multi-component non-stationary simulation signal is taken as an example to describe in detail the implementation and advantages of the present invention.

[0086] Suppose s(t) is a multi-component signal containing Gaussian white noise:

[0087] s(t)=s d (t)+n(t)

[0088] s d (t)=cos(20πt)+sin(200πt)+sin(400πt)+sin(100π(t-0.5) 2 )

[0089] In the formula, n(t) represents Gaussian white noise; s d (t) is an ideal multi-component signal. Set the sampling frequency to 1kHz, the sampling time to 1s, and the data length to 1000. Set the window length 2T=128, and the kernel function volume limit is β=5.

[0090] The process of the present invention is as figure 1 As shown, including the following steps:

[0091] Step A: Prepare the signal s(t) to be processed, the sampling frequency of which is f s , The data length is N;

[0092] Step B: Perform NMD analysis on the signal s(t);

[0093] Step B-1: Calculate the wavelet transform (Wavelet Transform, WT) W of the sign...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a time-frequency analysis method based on nonlinear mode decomposition and adaptive optimal kernel; the time-frequency analysis method combines the advantages in nonlinear mode decomposition analysis method and those in adaptive optimal kernel and comprises: decomposing a multi-component nonstable signal into a group of physically significant nonlinear mode components by using nonlinear mode composition algorithm, wherein the requirement on the anti-noise performance of subsequent analysis method is lowered since the algorithm has high noise robustness; then, enabling a kernel function to change adaptively with signal changes by using the time-frequency analysis method of adaptive optimal kernel, so that cross terms are effectively inhibited and time-frequency concentrating capacity is improved. The novel analysis method inherits the advantages in both nonlinear mode composition method and adaptive optimal kernel analysis method, and has excellent performances.

Description

Technical field [0001] The invention relates to the field of time-frequency analysis of non-stationary signals, in particular to a time-frequency analysis method based on nonlinear mode decomposition and an adaptive optimal kernel. Background technique [0002] The role of a good time-frequency analysis method in the estimation and analysis of non-stationary signals is self-evident. The signal itself has many attributes, and for signal estimation, frequency domain characteristics are very important attributes. [0003] There are many signal processing and estimation methods. The most classic is of course the Fourier transform, which can reflect the frequency characteristics of the signal. The Fourier transform has good frequency domain resolution capabilities for stationary signals. After the transformation, the signal spectrum can be obtained, but the Fourier transform The time information is lost, that is, the time when each frequency spectrum appears is not known, so the Fourie...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G01R23/16
CPCG01R23/16
Inventor 邵杰张鑫黄跃杨恬甜
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com