计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
19期
227-231
,共5页
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
Mu-比例歸一化均方誤差算法(MPNLMS)%稀疏控製%集員濾波%迴波消除
Mu-비례귀일화균방오차산법(MPNLMS)%희소공제%집원려파%회파소제
Mu-Proportionate Normalized Least Mean Square(MPNLMS)%sparseness controlled%set membership filtering%echo cancellation
稀疏控制算法将稀疏性系数加入到步长控制因子递推计算过程中，加速了传统回声消除算法的收敛速度。但其快速收敛与低复杂度是一对矛盾的需求。针对这一矛盾，提出了一种基于集员滤波的稀疏控制MPNLMS算法（SM-SCMPNLMS）。该算法中只有当参数估计误差大于给定的误差门限时滤波器系数才进行迭代更新，从而有效地减少了滤波器系数的迭代次数。在稀疏、色散路径以及路径突变三种环境下进行了仿真，结果表明新算法在降低计算复杂度的同时，表现出了与稀疏控制MPNLMS算法同样优良的收敛速度和稳态回波返回损失强度。
稀疏控製算法將稀疏性繫數加入到步長控製因子遞推計算過程中，加速瞭傳統迴聲消除算法的收斂速度。但其快速收斂與低複雜度是一對矛盾的需求。針對這一矛盾，提齣瞭一種基于集員濾波的稀疏控製MPNLMS算法（SM-SCMPNLMS）。該算法中隻有噹參數估計誤差大于給定的誤差門限時濾波器繫數纔進行迭代更新，從而有效地減少瞭濾波器繫數的迭代次數。在稀疏、色散路徑以及路徑突變三種環境下進行瞭倣真，結果錶明新算法在降低計算複雜度的同時，錶現齣瞭與稀疏控製MPNLMS算法同樣優良的收斂速度和穩態迴波返迴損失彊度。
희소공제산법장희소성계수가입도보장공제인자체추계산과정중，가속료전통회성소제산법적수렴속도。단기쾌속수렴여저복잡도시일대모순적수구。침대저일모순，제출료일충기우집원려파적희소공제MPNLMS산법（SM-SCMPNLMS）。해산법중지유당삼수고계오차대우급정적오차문한시려파기계수재진행질대경신，종이유효지감소료려파기계수적질대차수。재희소、색산로경이급로경돌변삼충배경하진행료방진，결과표명신산법재강저계산복잡도적동시，표현출료여희소공제MPNLMS산법동양우량적수렴속도화은태회파반회손실강도。
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 coeffi-cients 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 enhance-ment 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.
