数据分段输入的低秩自适应滤波方法  

Segmented Data-Inputting Based Low-rank Adaptive Filtering

作　　者：孙晓昶[1] 皇埔堪[1] 陈强 

机构地区：[1]国防科技大学四院信号处理研究室 [2]总参61通信研究所

出　　处：《信号处理》2003年第4期373-376,共4页Journal of Signal Processing

基　　金：国防预研项目编号:41321090202

摘　　要：针对高采样率下的高阶自适应滤波器难以实时实现的问题,本文提出了一种数据分段输入的降秩自适应滤波方法,其实现机理与传统的LMS和RLS方法勾不相同,其收敛速度与RLS方法基本相当,运算量却比RLS方法要小,其显著优势是大大降低了对物理处理器的速度要求。This paper proposes a low-rank adaptive filter of a different scheme from LMS and RLS algorithm mainly for high-order adaptive filtering under high sampling rate. The proposed filter converges almost as fast as RLS but the computational burden is less than the RLS. In most applications the demand for high capacity of the physical processor can be reached by adjusting the intervals between the segmented inputs.

关 键 词：数字信号处理 低秩自适应滤波方法 物理处理器 数据分段输入 LMS算法 RLS算法 

分 类 号：TN911.72[电子电信—通信与信息系统]