A modified adaptive weight-constrained FxLMS algorithm for feedforward active noise control systems. (July 2020) - Meng, Hao; Chen, Shuming
Abstract: The weight-constrained filtered-x least mean square (CFxLMS) algorithm with a fixed parameter shows slow convergence speed and weak noise reduction performance under certain circumstances. In order to solve this problem, a generalized modified adaptive CFxLMS (GMACFxLMS) algorithm is proposed to construct an adaptive weight-constrained parameter for the active noise control (ANC) systems. The GMACFXLMS algorithm is developed by using mixed operation of the Euclidean Norm of residual error e n and input noise signal X n . Different noise reduction effect will be achieved by choosing different coefficients of the Euclidean Norm of e n and X n . Especially, it can be utilized to deal with the ANC under impulse noise with symmetric α -stable ( S α S ) distribution environments. To further improve the performance of the GMACFxLMS algorithm, specifically for high impulse noise, we present an enhanced GMACFxLMS algorithm (EGMACFxLMS) with amplitude constraint of e n and X n . Simulation results demonstrate that the proposed algorithms achieve faster convergence rate and better noise reduction performance compared with other investigated algorithms. Moreover, the EGMACFxLMS algorithm exhibits the best noise reduction performance in high impulse noise input.