Variable step length factor construction method for LMS adaptive filtering

A technology of adaptive filtering and adaptive filter, which is applied in the direction of adaptive network, electrical components, impedance network, etc., to achieve fast convergence speed and overcome the effect of large fluctuation range

Active Publication Date: 2020-12-08
LOGISTICAL ENGINEERING UNIVERSITY OF PLA
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problems existing in the variable step size LMS adaptive filtering algorithm in the existing research, the present invention provides a variable step size factor construction method for LMS adaptive filtering, which is improved on the basis of the Sigmoid function and combined with normalization The LMS algorithm uses the errors at two moments before and after to establish a nonlinear relationship between the step factor and the error signal. The final effect is to obtain a better steady-state error while increasing 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
  • Variable step length factor construction method for LMS adaptive filtering
  • Variable step length factor construction method for LMS adaptive filtering
  • Variable step length factor construction method for LMS adaptive filtering

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0032] The algorithm flow of the present invention is as figure 1 As shown, a specific implementation is as follows:

[0033] 1) Use a sinusoidal signal with an amplitude of 10 and a frequency of 0.5rad / s as the original signal, use Gaussian white noise with a mean value of 0 and a variance of 1 as the random noise signal, and the signal length N is 200, and add the two to get the LMS The input signal of the adaptive filter, the expression of the input signal x(n) at time n is as follows,

[0034] x(n)=[x(n), x(n-1), ..., x(n-M+1)] T

[0035] Where n∈(M, N], the order M of the LMS adaptive filter is 20; the initialization step factor Among them, tr[ ] represents the trace of the matrix, that is, the sum of the diagonal elements of the matrix;

[0036] 2) Construct the desired signal d(n), first sample and delay the input signal to obtain x(n+50); then calculate the autocorrelation of the delayed signal to obtain r x (50); Finally, r x(50) Complete the construction of d(...

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 relates to the field of digital signal processing, in particular to a variable step length factor construction method for LMS (Least Mean Square) adaptive filtering. The method comprisesthe following steps: firstly, calculating the maximum value mu max of a variable step length factor at the moment n according to an input signal at the moment n so as to ensure the stability of an algorithm; then, calculating a step length factor at the moment n through a variable step length formula, finally, judging whether the step length factor at the moment n exceeds the maximum value mu maxof the variable step length factor at the moment n or not, if yes, taking mu max as the step length factor at the moment n, and if not, the LMS adaptive filtering method for constructing the variablestep length factor is high in convergence speed and small in steady-state error fluctuation amplitude.

Description

technical field [0001] The invention relates to the field of digital signal processing, in particular to a variable step factor construction method for LMS (Least Mean Square, least mean square) adaptive filtering. Background technique [0002] As one of the important branches in the field of digital signal processing, adaptive filtering technology has been widely used in the fields of radar, control, sonar, navigation system and industrial control after years of development. One of the most widely used is the least mean square adaptive filtering algorithm, that is, the LMS adaptive filtering algorithm. The LMS adaptive filtering algorithm is a search algorithm that simplifies the calculation of the gradient vector by making appropriate adjustments to the objective function. Due to its computational simplicity, the LMS adaptive filtering algorithm and others related to it have been widely used in various applications of adaptive filtering. [0003] However, in the LMS adap...

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): H03H21/00
CPCH03H21/0043H03H2021/0045Y02E40/40
Inventor 肖玮刘思蔚涂亚庆
Owner LOGISTICAL ENGINEERING UNIVERSITY OF PLA
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