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Improved proportional affine projection filtering method based on generalized correlation induction measurement

A generalized correlation and affine projection technology, applied in the field of sparse adaptive filtering, which can solve problems such as slow convergence of applications

Active Publication Date: 2020-03-17
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, in redundant system identification, these applications converge slower

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  • Improved proportional affine projection filtering method based on generalized correlation induction measurement
  • Improved proportional affine projection filtering method based on generalized correlation induction measurement
  • Improved proportional affine projection filtering method based on generalized correlation induction measurement

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Embodiment Construction

[0041] The technical scheme of the present invention is described in further detail below in conjunction with accompanying drawing, but protection scope of the present invention is not limited to the following description.

[0042] Such as figure 1 As shown, the improved proportional affine projection filtering method based on the generalized correlation induction metric includes the following steps:

[0043] S1. The expected weight of the filter The transpose of the input signal u(n)=[u(n),u(n-1),...,u(n-M+1)] of the filter at instant n T ∈R M x 1 Multiply and add the noise signal v(n) to get the desired output signal d(n):

[0044] d(n)=w 0 T u(n)+v(n);

[0045] In the formula, M represents the channel length;

[0046] S2. At each moment between instant n and instant n-K+1, repeat step S1 to obtain corresponding expected output signals d(n), d(n-1),...,d(n -K+1); and these desired output signals are formed into the desired output vector, and obtained:

[0047] D(n...

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Abstract

The invention discloses an improved proportional affine projection filtering method based on generalized correlation induction measurement. The method comprises the following steps: S1, acquiring an expected output signal; S2, acquiring an expected output vector; S3, constructing an input signal matrix and calculating a current actual output vector; S4, calculating an output error vector; S5, updating the current actual weight vector of the filter according to the calculated error vector; S6, adjusting updating parameters of a current actual weight vector of the filter based on generalized correlation induction measurement; and S7, taking the updated weight vector as a new weight vector of the filter, repeating the steps S1 to S6, and iteratively updating the weight vector of the filter. The improved proportional affine projection filtering method based on generalized correlation induction measurement has good filtering precision and low operation complexity.

Description

technical field [0001] The present invention relates to sparse adaptive filtering, in particular to an improved proportional affine projection filtering method based on generalized correlation induction metric. Background technique [0002] In recent years, Sparse Adaptive Filtering Algorithms (SAFAs) have received extensive attention because they can effectively identify unknown and sparse systems, where the impulse response to be characterized contains many coefficients close to zero. Compared with normalized least mean square (NLMS), proportional NLMS (P-NLMS) has faster convergence speed and better filtering accuracy in sparse system identification. In addition, applying the proportional method to the Affine Projection Algorithm (APA) to obtain the proportional APA (P-APA) can further improve the convergence speed and reduce the steady-state mismatch of P-NLMS when color input. [0003] However, the performance of the above algorithms will degrade when the system is dis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H21/00
CPCH03H21/0025H03H2021/0076
Inventor 李国亮赵集毛翔徐孝增乔景赐李谦张志鹏张洪斌
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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