基于改进NLMS算法的自适应滤波算法

Adaptive filtering algorithm based on improved NLMS adaptive algorithm

最小均方算法是应用最广泛的自适应算法之一,但其收敛速度欠佳。在传统NLMS算法的基础上,提出了重复调整归一化最小均方算法(DRNLMS)即在相邻两输入信号样本的间隔时间进行额外调整运算,以提高算法的收敛性,并通过计算机仿真实现该算法。

Least mean square algorithm is one of the most widely used adaptive algorithms in adaptive filtering, but its poor conver- gence performance. On the basis of traditional NLMS algorithm, Data-reusing normalized least mean square algorithm （DRNLMS） was put forward, i.e. input signal samples in adjacent two additional adjustment time interval of computing, to improve convergence per- formance, and realize this algorithm through computer simulation.