An Adaptive Filtering Method Based on Bias Compensation Auxiliary Variables

A technology of adaptive filtering and auxiliary variables, applied in the direction of adaptive network, electrical components, impedance network, etc., can solve problems such as failure to work normally, achieve unbiased estimation, expand the application range, and stabilize the method

Active Publication Date: 2022-07-15
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the defect that the existing deviation compensation RLS method cannot work normally under the condition of colored output noise, and proposes an adaptive filtering method based on deviation compensation auxiliary variables, which realizes that there are Unbiased Estimation of Unknown System in the Case of Additive Noise Interference with Unknown Variance

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
  • An Adaptive Filtering Method Based on Bias Compensation Auxiliary Variables
  • An Adaptive Filtering Method Based on Bias Compensation Auxiliary Variables
  • An Adaptive Filtering Method Based on Bias Compensation Auxiliary Variables

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0058] In this embodiment, the mean square error criterion is used as the performance index, which is used for system identification for noisy input in a colored output noise environment.

[0059] figure 1 What is given is the EIV-IIR filter model adopted by the present invention, which is an adaptive filter model under the system identification framework. Combine below figure 1 Describe the specific implementation of the adaptive filtering method based on the bias compensation auxiliary variable proposed in the present invention, which is summarized as follows:

[0060] Step A, presetting the number of iterations M, constructing a variable error model;

[0061] Among them, the variable containing error model, namely the EIV-IIR filter model, can be expressed as:

[0062]

[0063] Among them, EIV is Errors in Variables, which means the variable contains errors; IIR is Infinite ImpulseResponse, which is the infinite impulse response; y(i) is the noisy output signal at the...

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 an adaptive filtering method based on a bias compensation auxiliary variable, belonging to the technical field of signal estimation and digital filters. The method includes: presetting the number of iterations M and constructing a variable containing error model; constructing a class auxiliary variable strongly correlated with the input signal vector; calculating the estimated value and estimated deviation of the class auxiliary variable method under the condition of colored output noise; The estimated value of the unknown input noise variance under the condition of output noise; the deviation caused by the noise is compensated by the principle of deviation compensation, and the unbiased estimate of the unknown system parameters is obtained. The method can work stably when the input signal is a white Gaussian process or a colored Gaussian process, and the output noise signal is colored noise; and the influence of the output noise variance is eliminated by introducing a class auxiliary variable, only the input noise variance needs to be estimated, and the method is reduced. A real-time method for estimating the variance of input noise is proposed, which can more accurately estimate the variance of unknown input noise.

Description

technical field [0001] The invention relates to an adaptive filtering method based on a bias compensation auxiliary variable, belonging to the technical field of signal estimation and digital filters. Background technique [0002] The traditional RLS adaptive filter has a wide range of applications in the field of adaptive filtering, such as adaptive equalization, echo cancellation, antenna array beamforming, parameter estimation, noise cancellation, and spectrum estimation in the communication field. [0003] Convergence speed, steady state offset and robustness are three important performance indicators of adaptive filter. The convergence speed determines the time it takes for the adaptive filter to approach the unknown system, the level of steady-state imbalance determines the estimation accuracy of the proposed method for the unknown system, and the robustness determines the scope and effectiveness of the proposed method. These three indicators simultaneously affects th...

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
Patent Type & Authority Patents(China)
IPC IPC(8): H03H21/00
CPCH03H21/0043
Inventor 贾丽娟李妍
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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