Filtered weight FxLMS adaptation algorithm: Analysis, design and implementation

In the Filtered‐x Least‐Mean‐Square (FxLMS)‐based Active Noise Control (ANC), the convergence speed of the adaptation process has a direct relationship to a scalar parameter, called the step size. There is a theoretical upper‐bound for the step size beyond which the system becomes unstable. However, the step size is usually set to a number smaller than its upper‐bound in practice. This is because for relatively large step sizes, the adaptation process becomes very sensitive to any non‐stationary change in acoustic noise. Owing to this trade‐off, real‐time implementation of high‐performance ANC systems becomes challenging. To overcome this problem, this paper develops a novel ANC algorithm in which a recursive filter compensates for influences of the step size increase on the system performance. It is shown that this filter can efficiently increase the step size upper‐bound; consequently, the performance of the system is improved. This improvement is demonstrated using computer simulation. Also, experimental results shows the preference of the proposed algorithm to the traditional FxLMS‐based ANC algorithm in practice. Copyright © 2011 John Wiley & Sons, Ltd.