Self-adaptive combination filtering method under impact noise condition

A technology of impact noise and joint filtering, applied in the direction of adaptive network, impedance network, electrical components, etc., can solve the problem of not using the approximate sparse characteristics of impact noise

Active Publication Date: 2016-12-21
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Although the extremely improved algorithms of LMP and NLMP can deal with impulsive noise to a certain extent, they still do not take advantage of the approximate sparseness of the impulsive noise itself

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  • Self-adaptive combination filtering method under impact noise condition
  • Self-adaptive combination filtering method under impact noise condition
  • Self-adaptive combination filtering method under impact noise condition

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Embodiment Construction

[0054] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0055] Technical scheme of the present invention is as follows:

[0056] 1. Adaptive joint sparse filtering algorithm under impact noise.

[0057] The main difference between the α-stable distribution and the Gaussian distribution is the tailing phenomenon, which is why the α-stable distribution is very suitable for describing the impact noise. In addition, the stable distribution is also very easy to control, and its probability density function is expressed as ψ(t)=exp{jut-γ|t| α [1+jβsgn(t)f(t,α)]},

[0058] in 00.

[0059] · α is the characteristic index, indicating the thickness of the tail of the distribution. The smaller the value of α, the thicker the tail and the stronger the impact. When ...

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Abstract

The invention discloses a self-adaptive combination filtering method under an impact noise condition. According to researches, an impact noise has an approximate sparse characteristic, and according to the approximate sparse characteristic, the impact noise has large amplitude in limited time, and has small amplitude in other time. A group sparse characteristic is shown, and according to the group sparse characteristic, most of sample values of signals in a time domain are zero, and at the same time, nonzero sample values occur in a grouped / clustered way. By using the characteristic of the impact noise and combining with the own characteristics of the signals, a target function is reconstructed, and a signal / noise combination estimation algorithm is designed to improve signal recovery quality. By adopting algorithm theoretical analysis and computer simulated analysis, the provided algorithm performance is more excellent, and a good application prospect is provided in a parameter estimation field, a voice signal processing field, and other fields.

Description

technical field [0001] The invention relates to a method for jointly estimating sparse time-varying signals polluted by impact noise and impact noise, in particular to an adaptive joint filtering method under impact noise. Background technique [0002] The adaptive filtering algorithm is an important branch in the field of adaptive signal processing. The purpose is to introduce some optimal criterion in the signal processing, and automatically adjust the filter coefficients through the optimal criterion, so that the output can contain the expected signal as much as possible. A specific objective function is minimized. The Least Mean Squares algorithm is an adaptive filtering algorithm proposed by Widrow and Holf in 1960. [1] , because of its simple structure, low computational complexity, and easy convergence in a stationary environment, it has been widely used in the fields of system identification, channel equalization, signal enhancement, and prediction. [0003] In the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H21/00
CPCH03H21/0012H03H21/0043
Inventor 刘宏清杨威黎勇周翊
Owner CHONGQING UNIV OF POSTS & TELECOMM
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