Robust signal estimation using H∞ criteria

S. Tavathia; J. F. Doherty

doi:10.1109/ICASSP.1994.389764

Robust signal estimation using H_∞ criteria

S. Tavathia
, J. F. Doherty

Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

Kalman and Weiner filters are used extensively for implementation of optimal filters. These filters try to minimize variance of error when input signal and noise power spectral density (PSD) is known. Here we consider filters under H_∞ setting. This paper shows the robust performance of H_∞ filters under noise uncertainty. Optimization criteria for H_∞ filters is represented in the time and frequency domain. From this representation it is shown that optimal H_∞ filters place an upper bound on error PSD and error variance for a certain class of noise perturbations. It is shown that the estimation problem can be reduced to the model matching problem, which can be solved using 7 - iteration in the frequency domain. Simulations results are included to confirm the robust performance of these filters for noise PSD belonging to a certain class.

Original language	English (US)
Article number	389764
Pages (from-to)	IV525-IV528
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
DOIs	https://doi.org/10.1109/ICASSP.1994.389764
State	Published - 1994
Event	Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust Duration: Apr 19 1994 → Apr 22 1994

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP.1994.389764

Cite this

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abstract = "Kalman and Weiner filters are used extensively for implementation of optimal filters. These filters try to minimize variance of error when input signal and noise power spectral density (PSD) is known. Here we consider filters under H∞ setting. This paper shows the robust performance of H∞ filters under noise uncertainty. Optimization criteria for H∞ filters is represented in the time and frequency domain. From this representation it is shown that optimal H∞ filters place an upper bound on error PSD and error variance for a certain class of noise perturbations. It is shown that the estimation problem can be reduced to the model matching problem, which can be solved using 7 - iteration in the frequency domain. Simulations results are included to confirm the robust performance of these filters for noise PSD belonging to a certain class.",

author = "S. Tavathia and Doherty, \{J. F.\}",

note = "Publisher Copyright: {\textcopyright} 1994 IEEE.; Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) ; Conference date: 19-04-1994 Through 22-04-1994",

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doi = "10.1109/ICASSP.1994.389764",

language = "English (US)",

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T1 - Robust signal estimation using H∞ criteria

AU - Tavathia, S.

AU - Doherty, J. F.

PY - 1994

Y1 - 1994

N2 - Kalman and Weiner filters are used extensively for implementation of optimal filters. These filters try to minimize variance of error when input signal and noise power spectral density (PSD) is known. Here we consider filters under H∞ setting. This paper shows the robust performance of H∞ filters under noise uncertainty. Optimization criteria for H∞ filters is represented in the time and frequency domain. From this representation it is shown that optimal H∞ filters place an upper bound on error PSD and error variance for a certain class of noise perturbations. It is shown that the estimation problem can be reduced to the model matching problem, which can be solved using 7 - iteration in the frequency domain. Simulations results are included to confirm the robust performance of these filters for noise PSD belonging to a certain class.

AB - Kalman and Weiner filters are used extensively for implementation of optimal filters. These filters try to minimize variance of error when input signal and noise power spectral density (PSD) is known. Here we consider filters under H∞ setting. This paper shows the robust performance of H∞ filters under noise uncertainty. Optimization criteria for H∞ filters is represented in the time and frequency domain. From this representation it is shown that optimal H∞ filters place an upper bound on error PSD and error variance for a certain class of noise perturbations. It is shown that the estimation problem can be reduced to the model matching problem, which can be solved using 7 - iteration in the frequency domain. Simulations results are included to confirm the robust performance of these filters for noise PSD belonging to a certain class.

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DO - 10.1109/ICASSP.1994.389764

M3 - Conference article

AN - SCOPUS:85079120254

SN - 1520-6149

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JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

M1 - 389764

T2 - Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6)

Y2 - 19 April 1994 through 22 April 1994

ER -

Robust signal estimation using H_∞ criteria

Abstract

All Science Journal Classification (ASJC) codes

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