Improved natural gradient variable step-size blind source separation algorithm

A technology of blind source separation and natural gradient, applied in computing, computer parts, instruments, etc., can solve the problems of convergence speed and steady-state error can not be balanced

Inactive Publication Date: 2015-08-26
TAIYUAN UNIV OF TECH
View PDF2 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem that the existing blind source separation algorithm cannot reach a balance between the convergence speed

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
  • Improved natural gradient variable step-size blind source separation algorithm
  • Improved natural gradient variable step-size blind source separation algorithm
  • Improved natural gradient variable step-size blind source separation algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0013] The improved natural gradient variable step size blind source separation algorithm includes the following steps:

[0014] The first step: collect the source signal s(t) and randomly generate the mixing matrix A, use the mixing matrix A to linearly mix the source signal s(t) to obtain the mixed signal x(t), and collect the mutation signal, FSK signal and speech signal Three non-stationary source signals s(t), randomly generate a 3×3-dimensional mixing matrix A to mix the source signals, and obtain a mixed signal x(t), that is, x(t)=As(t), the mixing matrix A Obey the uniform distribution on the interval [-1, 1], and the sampling frequency is 10kHz;

[0015] The second step: the separation matrix W(t), autocorrelation matrix R x (t) and learning step size μ(t) initialization, initialize the separation matrix W 0 =0.5*I 3×3 , initialize the autocorrelation matrix R x (0)=I, initialize the learning step size μ 0 =0.03;

[0016] The third step: add orthogonal constrain...

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 belongs to the technical field of blind source separation and specifically relates to an improved natural gradient variable step-size blind source separation algorithm. The algorithm mainly comprises the following steps of randomly generating a hybrid matrix, carrying out linear mixing on source signals, and obtaining three hybrid signals; initializing a separation matrix, an autocorrelation matrix and a learning step, adding an orthogonal constraint, and controlling a variable step size by instantaneous errors and carrying out iteration; calculating an estimation signal; and drawing a recovered source signal waveform according to the estimation signal. The invention provides the natural gradient blind source separation algorithm based on the orthogonal constraint, the algorithm constrains the strength of recovered signals, and the stability of a separation process under an unstable environment is ensured; in addition, the convergence is accelerated, the separation precision is improved, the signal amplitude under the unstable environment constantly changes, the change frequency is relatively high, and a conventional separation method can not perform the separation, so that the related technology of the invention is advanced and prospective.

Description

technical field [0001] The invention belongs to the technical field of Blind Source Separation (BSS), and in particular relates to an improved blind source separation algorithm with variable step size of natural gradient. Background technique [0002] At present, the blind signal separation algorithm based on Independent Component Analysis (ICA) is widely used in signal processing fields such as biomedical engineering, speech enhancement, communication system and mechanical fault diagnosis. Blind source separation algorithm can be divided into adaptive algorithm and batch algorithm according to the specific solution. The batch algorithm has good numerical stability, but it needs to know a large amount of observation data in advance, so it is not suitable for real-time online signal separation. The adaptive algorithm has a small amount of calculation, short calculation time, and is suitable for non-stationary environments, so it is widely used. The algorithm mainly includes ...

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): G06K9/62
CPCG06F18/2134
Inventor 张锦刘婷李灯熬
Owner TAIYUAN UNIV OF TECH
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
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap