IEICE TRANSACTIONS on Fundamentals
A Cascade Lattice IIR Adaptive Filter for Total Least Squares Problem
Summary :
In many actual applications of the adaptive filtering, input signals as well as output signals often contain observation noises. Hence, it is necessary to develop an adaptive filtering algorithm to such an errors-in-variables (EIV) model. One solution for identifying the EIV model is a total least squares (TLS) algorithm based on a singular value decomposition of an off-line processing. However, it has not been considered to identify the EIV IIR system using an adaptive TLS algorithm of which stability has been guaranteed during adaptation process. Hence we propose a normalized lattice IIR adaptive filtering algorithm for the TLS parameter estimation. We also show the effectiveness of the proposed algorithm under noisy circumstances through simulations.
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- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.8 pp.1151-1156
- Publication Date
- 1996/08/25
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- Special Section PAPER (Special Section on Digital Signal Processing)
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Jun'ya SHIMIZU, Yoshikazu MIYANAGA, Koji TOCHINAI, "A Cascade Lattice IIR Adaptive Filter for Total Least Squares Problem" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 8, pp. 1151-1156, August 1996, doi: .
Abstract: In many actual applications of the adaptive filtering, input signals as well as output signals often contain observation noises. Hence, it is necessary to develop an adaptive filtering algorithm to such an errors-in-variables (EIV) model. One solution for identifying the EIV model is a total least squares (TLS) algorithm based on a singular value decomposition of an off-line processing. However, it has not been considered to identify the EIV IIR system using an adaptive TLS algorithm of which stability has been guaranteed during adaptation process. Hence we propose a normalized lattice IIR adaptive filtering algorithm for the TLS parameter estimation. We also show the effectiveness of the proposed algorithm under noisy circumstances through simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_8_1151/_p
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@ARTICLE{e79-a_8_1151,
author={Jun'ya SHIMIZU, Yoshikazu MIYANAGA, Koji TOCHINAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Cascade Lattice IIR Adaptive Filter for Total Least Squares Problem},
year={1996},
volume={E79-A},
number={8},
pages={1151-1156},
abstract={In many actual applications of the adaptive filtering, input signals as well as output signals often contain observation noises. Hence, it is necessary to develop an adaptive filtering algorithm to such an errors-in-variables (EIV) model. One solution for identifying the EIV model is a total least squares (TLS) algorithm based on a singular value decomposition of an off-line processing. However, it has not been considered to identify the EIV IIR system using an adaptive TLS algorithm of which stability has been guaranteed during adaptation process. Hence we propose a normalized lattice IIR adaptive filtering algorithm for the TLS parameter estimation. We also show the effectiveness of the proposed algorithm under noisy circumstances through simulations.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - A Cascade Lattice IIR Adaptive Filter for Total Least Squares Problem
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1151
EP - 1156
AU - Jun'ya SHIMIZU
AU - Yoshikazu MIYANAGA
AU - Koji TOCHINAI
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1996
AB - In many actual applications of the adaptive filtering, input signals as well as output signals often contain observation noises. Hence, it is necessary to develop an adaptive filtering algorithm to such an errors-in-variables (EIV) model. One solution for identifying the EIV model is a total least squares (TLS) algorithm based on a singular value decomposition of an off-line processing. However, it has not been considered to identify the EIV IIR system using an adaptive TLS algorithm of which stability has been guaranteed during adaptation process. Hence we propose a normalized lattice IIR adaptive filtering algorithm for the TLS parameter estimation. We also show the effectiveness of the proposed algorithm under noisy circumstances through simulations.
ER -
English
