Active impulsive noise control algorithm with post adaptive filter coefficient filtering

• Published in: IET signal processing Vol. 7; no. 6; pp. 515 - 521
• Main Authors: Wu, Lifu, Qiu, Xiaojun
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.08.2013; John Wiley & Sons, Inc
• Subjects: Active control; active impulsive noise control algorithm; active noise control; Adaptive filters; Algorithms; Coefficients; Cost function; dynamic range; error signals; filter coefficients vector; filtered weight filtered‐x normalised least mean square algorithm; first‐order infinite impulse response filter; IIR filter; IIR filters; least mean squares methods; Mathematical analysis; Noise control; nonlinear transformations; post adaptive filter coefficient filtering; Special Issue on Active Noise Control: Theory, Review, and Applications; squared Euclidean norm; system stability; transforms; Vectors (mathematics); system stability; least mean squares methods; adaptive filters; active noise control; IIR filter; post adaptive filter coefficient filtering; squared Euclidean norm; IIR filters; error signals; transforms; dynamic range; nonlinear transformations; filter coefficients vector; cost function; active impulsive noise control algorithm; filtered weight filtered-x normalised least mean square algorithm; first-order infinite impulse response filter
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2012.0164

Most algorithms for active impulsive noise control employ non-linear transformations to limit the reference and/or error signals and to maintain system stability. From a more direct manner, a new cost function is proposed in this study which is defined as the summation of the squared Euclidean norm of difference between the currently updated filter coefficients vector and all past filter coefficients vector subject to the constraint imposed on the adaptive filter output. A new adaptive algorithm is derived from the cost function and called the filtered weight filtered-x normalised least mean square algorithm because it can be interpreted as a post filter structure which passes the filter coefficients through a first-order infinite impulse response filter. The proposed algorithm is suitable for active control of impulsive noise since it directly limits the dynamic range of the adaptive filter coefficients and prevents heavy fluctuation of the filter coefficients. Simulations compare the performance of the proposed algorithm with the existing algorithms and demonstrate the effectiveness of the proposed algorithm.