摘要
针对传统的最小均方(LMS)自适应滤波算法在高速采样系统的信号处理中存在着收敛性慢,系统易受外界干扰等难以避免的缺点,提出了一种基于前向差分的Delta算子方法所描述的最小均方自适应滤波算法。该算法利用前向差分的Delta变换推导出Delta-LMS滤波器的结构及其迭代步骤,与传统的基于移位算子所描述的LMS算法和基于后向差分的Delta算子所描述的LMS算法相比,有效地加快了系统的收敛速度,改善了系统的跟踪性能。仿真结果表明,该算法在高速采样系统的信号处理中具有更快的收敛速度和更好的滤波性能。
Because the traditional least mean square （LMS） filtering algorithm using q-operator has disvantages of the slow convergence and the interference from the outside noise of the system and so on, an adapting LMS algorithm using the forward delta operator is presented. The algorithm obtains iterative steps of the Delta-LMS algorithm and the filter construction by the forward Delta operator. And it is better than the traditional filtering algorithm and the back- ward Delta operator on accelerating the convergence of the system. Simulation results verify that the algorithm has the faster convergence speed and the better filtering performance in high speed signal processing.
出处
《数据采集与处理》
CSCD
北大核心
2009年第2期189-192,共4页
Journal of Data Acquisition and Processing
