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We present a new family of algorithms that solve the bias problem in the equation-error based adaptive infinite impulse response (IIR) filtering. A novel constraint, called the *constant-norm constraint*, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherited and the shortcomings are overcome. A new cost function based on the *constant-norm constraint* and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family are proposed. The analysis of the stationary points is presented to show that the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the proposed methods indeed produce unbiased parameter estimation, while being simple both in computation and implementation.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.5 pp.1273-1279

- Publication Date
- 2001/05/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Digital Signal Processing

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Hyun-Chool SHIN, Woo-Jin SONG, "Bias-Free Adaptive IIR Filtering" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 5, pp. 1273-1279, May 2001, doi: .

Abstract: We present a new family of algorithms that solve the bias problem in the equation-error based adaptive infinite impulse response (IIR) filtering. A novel constraint, called the *constant-norm constraint*, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherited and the shortcomings are overcome. A new cost function based on the *constant-norm constraint* and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family are proposed. The analysis of the stationary points is presented to show that the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the proposed methods indeed produce unbiased parameter estimation, while being simple both in computation and implementation.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_5_1273/_p

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@ARTICLE{e84-a_5_1273,

author={Hyun-Chool SHIN, Woo-Jin SONG, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Bias-Free Adaptive IIR Filtering},

year={2001},

volume={E84-A},

number={5},

pages={1273-1279},

abstract={We present a new family of algorithms that solve the bias problem in the equation-error based adaptive infinite impulse response (IIR) filtering. A novel constraint, called the *constant-norm constraint*, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherited and the shortcomings are overcome. A new cost function based on the *constant-norm constraint* and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family are proposed. The analysis of the stationary points is presented to show that the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the proposed methods indeed produce unbiased parameter estimation, while being simple both in computation and implementation.},

keywords={},

doi={},

ISSN={},

month={May},}

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TY - JOUR

TI - Bias-Free Adaptive IIR Filtering

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 1273

EP - 1279

AU - Hyun-Chool SHIN

AU - Woo-Jin SONG

PY - 2001

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E84-A

IS - 5

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - May 2001

AB - We present a new family of algorithms that solve the bias problem in the equation-error based adaptive infinite impulse response (IIR) filtering. A novel constraint, called the *constant-norm constraint*, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherited and the shortcomings are overcome. A new cost function based on the *constant-norm constraint* and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family are proposed. The analysis of the stationary points is presented to show that the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the proposed methods indeed produce unbiased parameter estimation, while being simple both in computation and implementation.

ER -