2-D Adaptive Autoregressive Modeling Using New Lattice Structure

Takayuki NAKACHI; Katsumi YAMASHITA; Nozomu HAMADA

2-D Adaptive Autoregressive Modeling Using New Lattice Structure

Takayuki NAKACHI, Katsumi YAMASHITA, Nozomu HAMADA

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The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.8 pp.1145-1150

Publication Date: 1996/08/25

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Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)

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Takayuki NAKACHI, Katsumi YAMASHITA, Nozomu HAMADA, "2-D Adaptive Autoregressive Modeling Using New Lattice Structure" in IEICE TRANSACTIONS on Fundamentals, vol. E79-A, no. 8, pp. 1145-1150, August 1996, doi: .
Abstract: The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_8_1145/_p

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@ARTICLE{e79-a_8_1145,
author={Takayuki NAKACHI, Katsumi YAMASHITA, Nozomu HAMADA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={2-D Adaptive Autoregressive Modeling Using New Lattice Structure},
year={1996},
volume={E79-A},
number={8},
pages={1145-1150},
abstract={The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - 2-D Adaptive Autoregressive Modeling Using New Lattice Structure
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1145
EP - 1150
AU - Takayuki NAKACHI
AU - Katsumi YAMASHITA
AU - Nozomu HAMADA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1996
AB - The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.
ER -