Multiparameter optimization of inverse filtering algorithms

Tamás Dabóczi, I. Kollár

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

This paper investigates inverse filtering of transient signals. The problem is ill-conditioned, which means that a small uncertainty in the measurement causes large deviations in the reconstructed signal. This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. Deconvolution algorithms are usually controlled by one or a few parameters. Several algorithms can be found in the literature to find the best setting of inverse filtering methods; however, usually methods with only one free parameter are handled. In this paper, an algorithm is proposed to optimize several parameters, on the basis of a spectral model. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, and to the noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and on experimental data.

Original languageEnglish
Pages (from-to)417-421
Number of pages5
JournalIEEE Transactions on Instrumentation and Measurement
Volume45
Issue number2
DOIs
Publication statusPublished - 1996

Fingerprint

optimization
Deconvolution
Noise abatement
noise reduction
deviation
causes
Uncertainty

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation

Cite this

Multiparameter optimization of inverse filtering algorithms. / Dabóczi, Tamás; Kollár, I.

In: IEEE Transactions on Instrumentation and Measurement, Vol. 45, No. 2, 1996, p. 417-421.

Research output: Contribution to journalArticle

@article{e9ea6bafc43c42e09a6ac056a411c088,
title = "Multiparameter optimization of inverse filtering algorithms",
abstract = "This paper investigates inverse filtering of transient signals. The problem is ill-conditioned, which means that a small uncertainty in the measurement causes large deviations in the reconstructed signal. This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. Deconvolution algorithms are usually controlled by one or a few parameters. Several algorithms can be found in the literature to find the best setting of inverse filtering methods; however, usually methods with only one free parameter are handled. In this paper, an algorithm is proposed to optimize several parameters, on the basis of a spectral model. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, and to the noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and on experimental data.",
author = "Tam{\'a}s Dab{\'o}czi and I. Koll{\'a}r",
year = "1996",
doi = "10.1109/19.492758",
language = "English",
volume = "45",
pages = "417--421",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

TY - JOUR

T1 - Multiparameter optimization of inverse filtering algorithms

AU - Dabóczi, Tamás

AU - Kollár, I.

PY - 1996

Y1 - 1996

N2 - This paper investigates inverse filtering of transient signals. The problem is ill-conditioned, which means that a small uncertainty in the measurement causes large deviations in the reconstructed signal. This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. Deconvolution algorithms are usually controlled by one or a few parameters. Several algorithms can be found in the literature to find the best setting of inverse filtering methods; however, usually methods with only one free parameter are handled. In this paper, an algorithm is proposed to optimize several parameters, on the basis of a spectral model. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, and to the noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and on experimental data.

AB - This paper investigates inverse filtering of transient signals. The problem is ill-conditioned, which means that a small uncertainty in the measurement causes large deviations in the reconstructed signal. This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. Deconvolution algorithms are usually controlled by one or a few parameters. Several algorithms can be found in the literature to find the best setting of inverse filtering methods; however, usually methods with only one free parameter are handled. In this paper, an algorithm is proposed to optimize several parameters, on the basis of a spectral model. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, and to the noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and on experimental data.

UR - http://www.scopus.com/inward/record.url?scp=0030129507&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030129507&partnerID=8YFLogxK

U2 - 10.1109/19.492758

DO - 10.1109/19.492758

M3 - Article

AN - SCOPUS:0030129507

VL - 45

SP - 417

EP - 421

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

IS - 2

ER -