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Improved Scale Affine Projection Filtering Method Based on Generalized Correlation Induced Metric

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

Active Publication Date: 2021-08-10
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 Scale Affine Projection Filtering Method Based on Generalized Correlation Induced Metric
  • Improved Scale Affine Projection Filtering Method Based on Generalized Correlation Induced Metric
  • Improved Scale Affine Projection Filtering Method Based on Generalized Correlation Induced Metric

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Embodiment Construction

[0041] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, but the 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×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: ...

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Abstract

The invention discloses an improved proportional affine projection filtering method based on a generalized correlation induction metric, comprising the following steps: S1. Obtaining a desired output signal; S2. Obtaining a desired output vector; S3. Constructing an input signal matrix and calculating The current actual output vector; S4. Calculate the output error vector; S5. Update the current actual weight vector of the filter according to the calculated error vector; S6. Update the current actual weight vector of the filter based on the generalized correlation induction metric Adjust the parameters; S7. Use the updated weight vector as a new weight vector of the filter, and repeat steps S1 to S6 to iteratively update the weight vector of the filter. The present invention provides an improved proportional affine projection filtering method based on generalized correlation induction metric, which has better filtering precision and lower computational complexity.

Description

technical field [0001] The invention relates to sparse adaptive filtering, in particular to an improved scale affine projection filtering method based on generalized correlation induction measure. Background technique [0002] In recent years, Sparse Adaptive Filtering Algorithms (SAFAs) have received a lot of 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 scaling 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 for color input. [0003] However, the performance of the above algorithms will degrade when the system is disturbed by non-Gaussia...

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Application Information

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Patent Type & Authority Patents(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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