NDLI: Adaptive EIV-FIR filtering against coloured output noise by using linear prediction technique

Content Provider	IET Digital Library
Author	Tang, Tang Jia, Lijuan Lou, Jian Tao, Ran Wang, Yue
Abstract	The problem of finite impulse response (FIR) filtering in errors-in-variables (EIV) system is studied. Due to the input noise, traditional recursive least-squares (RLS) algorithms are biased in EIV system. Most existing bias-compensated approaches are proposed in the case that both the input–output noises are white Gaussian random processes. However, taking account of the situation where the output is corrupted by coloured noise, there are rare existing algorithms work well. Two bias-compensated RLS algorithms with acceptable computational complexity are proposed, which can obtain unbiased real-time filtering in non-stationary system when the input noise is white while the output noise is coloured. Under the assumption that the input signal is a coloured process, linear prediction technique is used to estimate the sample of the input signal. Exploiting the statistical properties of the cross-correlation function between the least-squares error and the forward/backward prediction error, the input noise variance can be estimated and the bias can be compensated. Simulation results illustrate the good performance of the proposed algorithms.
Starting Page	104
Ending Page	112
Page Count	9
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 1, Feb (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0686
Journal	IET Signal Processing
Publisher Date	2017-08-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive EIV-FIR Filtering Adaptive Filter Coloured Output Noise Computational Complexity Error-in-variables System Filtering Method in Signal Processing Finite Impulse Response Filtering FIR Filter Forward-backward Prediction Error Gaussian Processes Input–output Noises Least-squares Error Linear Prediction Technique Random Processes Statistical Property Statistics White Gaussian Random Processes
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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