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- 2016
一种改进的PNLMS自适应滤波算法
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Abstract:
比例归一化最小均方算法PNLMS(proportionate NLMS)引入步长控制矩阵, 为滤波器不同的系数赋予不同的Proportionate步长, 从而加快了算法的初始收敛速度, 但其后期收敛速度下降, 甚至比NLMS收敛速度还慢．针对此问题提出一种改进的PNLMS算法, 通过定量分析滤波器系数的收敛过程, 在迭代过程中建立了Proportionate步长与滤波器当前系数幅值之间的非线性函数关系――倒数关系, 较大幅度地降低了算法的复杂度．仿真结果表明, 该算法的收敛速度和稳定性优于PNLMS算法及其改进算法MPNLMS, 并且算法的计算复杂度远低于MPNLMS算法．
Step control matrix is introduced into proportionate normalized least mean square(PNLMS)algorithm，which considerably improves the initial convergence speed by setting different proportionate steps for different filter coefficients. However，the later convergence speed becomes lower than that of NLMS. To solve this problem，an improved PNLMS algorithm is proposed. A nonlinear reciprocal relationship between proportionate step and the current amplitudes of filter coefficients is established by quantitatively analyzing the convergence process. The simulation shows that the improved PNLMS algorithm has faster convergence speed and better robustness than PNLMS and MPNLMS algorithms. Moreover，the computational complexity of the algorithm is greatly reduced compared with MPNLMS algorithm
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