关键词: Mu-比例归一化均方误差算法（MPNLMS）, 稀疏控制, 集员滤波, 回波消除

Abstract: Sparseness-controlled adaptive algorithm estimates the sparseness of an impulse response and allocates a higher weighting to the proportionate term in the gain matrix for a relatively more sparse impulse response compared to one which is less sparse. Such that it improves the convergence speed of traditional algorithm. However, the large number of filter coefficients in echo cancellation applications diminishes the usefulness of  this algorithm  owing to increased complexity. To deal with this problem and improve the computational efficiency, the novel SM-SCMPNLMS algorithm is presented by combining the Sparseness-Controlled law PNLMS algorithm（SCMPNLMS） and the framework of Set-Membership Filtering（SMF）. In SM-SCMPNLMS algorithm, the filter coefficients are updated such that the magnitude of the output estimation error is less than a pre-determined threshold. As a result, the proposed algorithm reduces overall computation complexity significantly due to sparse time update. Simulation results show the new algorithm has an attractive faster converge and echo return lossless enhancement for three situations of sparse, dispersive and varying channels. Furthermore, it reduces the overall computational complexity due to the data-selective feature of the SMF approach.

Key words: Mu-Proportionate Normalized Least Mean Square（MPNLMS）, sparseness controlled, set membership filtering, echo cancellation