A New $l_0$-LMS Algorithm With Adaptive Zero Attractor

摘要

In the norm constraint least mean square ( -LMS) algorithm, the zero attractor is an important parameter which balances the trade-off between the convergence rate and steady-state error of the algorithm. However, there is no practically effective choice guideline of this parameter. In addition, the optimal value of this parameter should be time-varying when the measurement noise power varies with time, and a fixed value of the zero attractor is no longer suitable. In this letter, we propose an -LMS algorithm with adaptive zero attractor for applications with time-varying measurement noise signal, where the zero attractor is updated based on the criterion of maximizing the decrease of the transient mean square deviation.