Polynomial Adaptive Active Noise Cancellation Method Based on Laguerre Structure

Abstract

The invention discloses a multinomial adaptive active noise cancellation method based on a Laguerre structure. The method comprises: a Laguerre time delay unit having the characteristics of an IIR (infinite impulse response) filter is used for replacing a time delay unit Z-1 in a Volterra filter, output by virtue of a laguerre time delay node is taken as input of a non-linear filter based on bounded constrain product coupling Volterra approximate structure, characteristic convergence conditions can be effectively improved, the system stability is guaranteed, the computation complexity is low, hardware is easy to realize; and an LMS (least mean square) adaptive algorithm with input signal and instant error normalized is adopted for adjusting filtering parameters, the input length of a filter can be further reduced while a non-linear adaptive filter is guaranteed to be stable, and the convergence rate is improved; especially under the condition that strong non-linear distortion exists, good anti-noise capacity is shown, so that the method is beneficial to popularization and application of an adaptive multinomial filter.

Description

technical field [0001] The invention relates to a polynomial self-adaptive active noise canceling method based on Laguerre structure, which is used for eliminating noise, intersymbol crosstalk and nonlinear distortion, and has good anti-noise performance. Background technique [0002] In recent years, with the development of modern signal processing theory, various nonlinear techniques have been successfully and widely used in spread spectrum communication, system identification, noise control, image processing and Therefore, nonlinear signal processing technology has attracted more and more attention from signal processing researchers at home and abroad, and it is also an important research direction in modern signal processing theory. Research shows that the optimal filtering in nonlinear, non-Gaussian or Gaussian background is nonlinear. Nonlinear adaptive filters often have better performance than linear adaptive filters, and due to their inherent nonlinear characterist...

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Problems solved by technology

Reference [4] "An Improved Volterra Adaptive Noise Canceller" (Zhang Xiumei, Zhao Zhijin, Shang Junna, Telecommunications Technology, Vol.34, No.2, pp.77-81, 2010) uses the Sigmoid function to Noise pretreatment, improved the approximate structure of Volterra above, and used it to construct a nonlinear adaptive noise canceller, which has better anti-noise performance, but the stability of the system cannot be guaranteed, and in order to get a better performance, increasing the length of its input signal, however this will lead to an increase in computational complexity

[0007] In summary, due to the disadvantages of complex calculation and system stability cannot be guaranteed, it will limit the practical application of the above-mentioned nonlinear adaptive filter in the active noise cancellation system

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