Forward/backward prediction solution for adaptive noisy FIR filtering  

作　　者：JIA LiJuan TAO Ran WANG Yue WADA Kiyoshi 

机构地区：[1]Department of Electronic Engineering [2] Beijing Institute of Technology [3] Beijing 100081 [4] China [5]Department of Electrical and Electronic System Engineering [6] Kyushu University [7] Fukuoka [8] Japan

出　　处：《Science in China(Series F)》2009年第6期1007-1014,共8页中国科学（F辑英文版）

基　　金：Supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant No 60625104);the Ministerial Foundation of China (Grant No A2220060039);the Fundamental Research Foundation of BIT (Grant No 1010050320810)

摘　　要：An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise.In this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation least squares （BCLS） methods are proposed.By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively.By exploiting the statistical properties of the cross-correlation function between the least squares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed.Simulation results are presented to illustrate the good performances of the proposed algorithms.An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise.In this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation least squares （BCLS） methods are proposed.By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively.By exploiting the statistical properties of the cross-correlation function between the least squares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed.Simulation results are presented to illustrate the good performances of the proposed algorithms.

关 键 词：adaptive FIR filtering recursive least squares algorithm bias compensation forward prediction backward prediction 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置] TN911.73[自动化与计算机技术—控制科学与工程]