Adaptive filter with variable parameters and zero attractors

An adaptive filter, zero-attraction technology, applied in adaptive networks, digital technology networks, impedance networks, etc., can solve the problem that the shape of the zero-attractor cannot be adjusted systematically, and achieve the effect of excellent convergence performance
CN109347457AActive Publication Date: 2019-02-15SUZHOU UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
SUZHOU UNIV
Publication Date
2019-02-15

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Abstract

The invention discloses an adaptive filter with variable parameters and zero attractors, and belongs to the field of digital filter design. The filter mainly uses time-varying step parameters and regularization parameters that control the intensity of the zero attractors to speed up the convergence speed of the adaptive filter and reduce its steady-state offset. The adaptive filter with variable parameters and zero attractors disclosed by the invention has a great improvement in performance compared with the conventional filter when the input is white signal input. The adaptive filter with variable parameters and zero attractors disclosed by the invention can be applied to applications such as echo cancellation and active noise control.
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Description

technical field

[0001] The invention discloses a variable parameter zero attractor adaptive filter, which belongs to the field of digital filter design. Background technique

[0002] Traditional least mean square filter (LMS) and normalized least mean square filter (NLMS) adaptive filters have a wide range of applications in adaptive filters, such as adaptive echo cancellation, active noise control and adaptive noise cancellation Wait. In some applications, the system estimated by the adaptive filter may be sparse. If the LMS or NLMS filter is used, when the step size parameter is large, the convergence speed of the adaptive filter is slow, and when the step size parameter is large When the time is small, the steady-state misadjustment of the adaptive filter is large.

[0003] In order to solve the above problems, the literature with l 1 Norm-constrained LMS filter (denoted as l 1 -LMS) [YChen, Y Gu, A O Hero. Sparse LMS for system identification, Proceedings of the IEEE...

Claims

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