Forward/backward prediction solution for adaptive noisy FIR filtering

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 prop...

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Bibliographic Details
Published in:Science China. Information sciences 2009-06, Vol.52 (6), p.1007-1014
Main Authors: Jia, LiJuan, Tao, Ran, Wang, Yue, Wada, Kiyoshi
Format: Article
Language:English
Subjects:
Algorithms
Bias
Computer Science
Cross correlation
Filtration
Impulse response
Information Systems and Communication Service
Least squares
参数估计
噪声滤波
最小二乘
有限脉冲响应
自适应
输出数据
预测误差
飞行情报
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Summary: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.
ISSN:1009-2757
1674-733X
1862-2836
1869-1919
DOI:10.1007/s11432-009-0086-9