Optimal Penalty Factor for the MOV-FxLMS Algorithm in Active Noise Control System

The minimum output variance filtered reference least mean square (MOV-FxLMS) algorithm is a effective algorithm that utilizes the penalty mechanism to help the active noise control (ANC) system achieve noise cancellation with constrained output variance or power. As it can constrain output power, the MOV-FxLMS algorithm can freely determine the ANC system's control effort, avoiding output saturation, and improving system stability. However, its performance is determined by a penalty factor, which is normally chosen by trial and error. Hence, this work proposes an optimal penalty factor and its feasible estimation that does not require any assumptions of Gaussian reference signal or input independence. This factor assists the MOV-FxLMS in achieving the optimal solution under the target output-variance constraint. Numerical simulations on measured paths demonstrate its effectiveness for various types of noise.

Automated summary

This study proposes an optimal penalty factor and its estimation method for achieving the optimal solution of the minimum output variance filtered reference least mean square (MOV-FxLMS) algorithm under the target output-variance constraint.