A variable momentum factor filtered-x weighted accumulated LMS algorithm for narrowband active noise control systems

• Published in: Measurement : journal of the International Measurement Confederation Vol. 48; pp. 282 - 291
• Main Authors: Bo, Zhong, Sun, Chao, Xu, Yonghui, Jiang, Shouda
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2014
• Subjects: Active noise control; Algorithms; Convergence; Cost function; FXLMS; Least mean squares algorithm; Mathematical analysis; Momentum factor; Narrowband; Narrowband active noise control (NANC); Remedies; Tracking; Momentum factor; Narrowband active noise control (NANC); FXLMS; Convergence
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2013.11.010

•The paper presents a new narrowband active noise control algorithm.•We compared the performance of this algorithm with conventional FXLMS and FXRLS algorithms.•The system convergence is significantly accelerated and the tracking capability is enhanced.•The computational complexity of the new algorithm is significantly less than that of the FXRLS algorithm. In this paper, a filtered-x weighted accumulated least mean square (FXWALMS) algorithm is proposed for a typical narrowband active noise control (NANC) system by introducing the momentum LMS (MLMS) algorithm into the conventional filtered-x LMS (FXLMS) algorithm. The algorithm uses the new cost function to derive the updating equation for the discrete Fourier coefficients (DFCs) of the secondary source. In this way, the proposed algorithm achieves fast convergence and tracking capabilities at the expense of degrading the steady-state performance. To remedy the problem of the poor steady-state performance for the FXWALMS algorithm, a scheme of using a variable momentum factor is proposed to improve the overall performance of the system. In addition, compared to the filtered-x recursive least squares (FXRLS) algorithm, the performance of the proposed algorithm is better in termsofthe computational complexity and tracking capabilities. Computer simulations were conducted to demonstrate the superior performance of the proposed algorithm in both stationary and non-stationary scenarios.