M-estimation impulsive noise active control method

Abstract

The invention discloses an M-estimation impulsive noise active control method. According to the M-estimation impulsive noise active control method, a mean square value of robust M-estimation of error signals is adopted as a cost function, and a threshold value is led into a reference signal channel to limit influence of an impulsive sample on the iterative process of the algorithm, and therefore, steady update of an coefficient of an adaptive filter can be ensured so as to achieve noise reduction. The M-estimation impulsive noise active control method includes the following steps that: (1) initializing the coefficient W (n) of the adaptive filter; (2) obtaining secondary waveform and the error e (n) of main noises; and (3) updating the coefficient of the filter: W(n=1)=W(n)+uq{e(n)}e(n)[S<^>(n)*Xc(n)], wherein n is a sampling time sequence, u is a convergence coefficient, q{e (n)} is M-estimation of the error signals, Xc(n) is a reference signal vector which has been subjected to threshold value processing, and S<^>(n) is an estimation model of a secondary channel transfer function. The M-estimation impulsive noise active control method provided by the invention can eliminate the influence of the impulsive sample on an adaptive filtering algorithm maximally, effectively improve the robustness to impulsive noises and noise reduction performance, and has a great practical value.

Description

technical field [0001] The invention relates to an active control method for impact noise based on M-estimation, in particular to an adaptive active control algorithm for impact noise, especially a feedforward adaptive control system for impact noise. Background technique [0002] Active noise control technology (ANC, Active Noise Control) has gradually become the main means of suppressing low-frequency noise problems, especially the development of digital signal processors (DSP) has greatly promoted the commercialization of ANC technology. The basic principle of ANC is that the control system emits secondary sound waves that are equal in amplitude to the noise waveform and opposite in phase to cancel each other. The classic ANC active control system generally uses an adaptive filter as the controller, and continuously adjusts the filter parameters through the filtering least mean square algorithm (FXLMS, Filtered-xLeast Mean Square). However, the FXLMS algorithm is propose...

AI Technical Summary

Problems solved by technology

However, the disadvantage is that the noise signal needs to be pre-statistically estimated (non-Gaussian α steady-state distribution) to determine the p value more accurately and the large amount of DSP calculation caused by the norm calculation

However, when the pulse signal of the reference signal channel and the error signal channel have a delay, the robustness of the algorithm will be greatly reduced

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