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Sparse promotion affine projection adaptive filter with low computational complexity

A technology of adaptive filter and computational complexity, applied in the direction of adaptive network, impedance network, electrical components, etc., can solve the problems of large computational complexity of the algorithm, divergence, slowing down of the convergence speed of the SNLMS algorithm, etc., to reduce the computational cost , the effect of improving the convergence speed

Active Publication Date: 2021-08-06
SUZHOU UNIV
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Problems solved by technology

In this algorithm, on the one hand, the introduction of the sparse promotion matrix brings a large computational complexity to the algorithm; on the other hand, when the colored signal is used as the input, the convergence speed of the SNLMS algorithm will slow down or even diverge.

Method used

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  • Sparse promotion affine projection adaptive filter with low computational complexity
  • Sparse promotion affine projection adaptive filter with low computational complexity
  • Sparse promotion affine projection adaptive filter with low computational complexity

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Embodiment

[0020] Its working process of the FSAPA filter that the present application proposes: first utilize the sparse signal recovery (SSR) field to develop l 2 and l 1 The iterative reweighting technique minimizes different diversity estimation functions, and then establishes the sparse promotion matrix S of the filter k ;Secondly, because the weight vector of the adaptive filter is updated slowly, the sparse promotion matrix is ​​updated periodically strategy; then update the scaling variables using an iterative reweighting framework with gradient descent Finally, the weight vector w of the filter is updated with the new scaling variable k+1 .

[0021] In this embodiment, a computer experiment method is used to verify the performance of the FSAPA filter. In the experiment, the FSAPA filter disclosed by the present invention is used to identify the unknown pseudo-sparse linear system in the environment of white noise interference, and its performance is compared with that of a...

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Abstract

The invention discloses a sparse promotion affine projection adaptive filter with low calculation complexity, and belongs to the field of digital filter design. The filter is provided by the application of a sparse regularization technology in the sparse signal recovery (SSR) field in an affine projection algorithm (APA). A sparse promotion matrix under different diversity estimation conditions is mainly adopted, so that the adaptive filter can better approach a sparse linear system. In addition, the sparse promotion matrix strategy is periodically updated, so that the calculation cost of the filter can be reduced, and the performance of the filter is not influenced. The sparse promotion affine projection adaptive filter with low computational complexity disclosed by the invention can be applied to electronic and communication systems with sparse characteristics.

Description

technical field [0001] The invention discloses an adaptive filter, in particular discloses a low-calculation complexity sparse-promoted affine projection adaptive filter, which belongs to the field of digital filter design. Background technique [0002] Adaptive signal processing is an important subject branch of modern signal processing technology, and has been widely used in radar, echo cancellation, image processing, communication and other fields. In practical engineering, the normalized least mean square algorithm (NLMS) and the affine projection algorithm (APA) have been widely used because of their small amount of calculation, easy implementation and perfect theoretical support. In the system response, the coefficients with zero or close to zero account for the vast majority, and the unknown system with only a small number of coefficients playing a significant role is called a sparse system. The sparse system identification problem is often involved in theory and eng...

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Application Information

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IPC IPC(8): H03H21/00
CPCH03H21/0043Y02D30/70
Inventor 倪锦根张宁宁
Owner SUZHOU UNIV
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