Multiparameter optimization of inverse filtering algorithms

Tamas Daboczi, I. Kollár

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper inverse filtering of transient signals is dealt width. The problem is ill-conditioned, which means that small uncertainty in the measurement causes large deviation 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. The deconvolution algorithms are usually controlled by one ore few number of 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. An algorithm is proposed, based on a spectral model, to optimize several parameters. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and experimental data.

Original languageEnglish
Title of host publicationConference Record - IEEE Instrumentation and Measurement Technology Conference
PublisherIEEE
Pages482-487
Number of pages6
Publication statusPublished - 1995
EventProceedings of the 1995 IEEE Instrumentation and Measurement Technology Conference - Naltham, MA, USA
Duration: Apr 23 1995Apr 26 1995

Other

OtherProceedings of the 1995 IEEE Instrumentation and Measurement Technology Conference
CityNaltham, MA, USA
Period4/23/954/26/95

Fingerprint

optimization
Deconvolution
Noise abatement
noise reduction
Ores
minerals
deviation
causes
Uncertainty

ASJC Scopus subject areas

  • Instrumentation

Cite this

Daboczi, T., & Kollár, I. (1995). Multiparameter optimization of inverse filtering algorithms. In Conference Record - IEEE Instrumentation and Measurement Technology Conference (pp. 482-487). IEEE.

Multiparameter optimization of inverse filtering algorithms. / Daboczi, Tamas; Kollár, I.

Conference Record - IEEE Instrumentation and Measurement Technology Conference. IEEE, 1995. p. 482-487.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Daboczi, T & Kollár, I 1995, Multiparameter optimization of inverse filtering algorithms. in Conference Record - IEEE Instrumentation and Measurement Technology Conference. IEEE, pp. 482-487, Proceedings of the 1995 IEEE Instrumentation and Measurement Technology Conference, Naltham, MA, USA, 4/23/95.
Daboczi T, Kollár I. Multiparameter optimization of inverse filtering algorithms. In Conference Record - IEEE Instrumentation and Measurement Technology Conference. IEEE. 1995. p. 482-487
Daboczi, Tamas ; Kollár, I. / Multiparameter optimization of inverse filtering algorithms. Conference Record - IEEE Instrumentation and Measurement Technology Conference. IEEE, 1995. pp. 482-487
@inproceedings{cf977f42893d4e2ebd39c50057432522,
title = "Multiparameter optimization of inverse filtering algorithms",
abstract = "In this paper inverse filtering of transient signals is dealt width. The problem is ill-conditioned, which means that small uncertainty in the measurement causes large deviation 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. The deconvolution algorithms are usually controlled by one ore few number of 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. An algorithm is proposed, based on a spectral model, to optimize several parameters. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and experimental data.",
author = "Tamas Daboczi and I. Koll{\'a}r",
year = "1995",
language = "English",
pages = "482--487",
booktitle = "Conference Record - IEEE Instrumentation and Measurement Technology Conference",
publisher = "IEEE",

}

TY - GEN

T1 - Multiparameter optimization of inverse filtering algorithms

AU - Daboczi, Tamas

AU - Kollár, I.

PY - 1995

Y1 - 1995

N2 - In this paper inverse filtering of transient signals is dealt width. The problem is ill-conditioned, which means that small uncertainty in the measurement causes large deviation 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. The deconvolution algorithms are usually controlled by one ore few number of 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. An algorithm is proposed, based on a spectral model, to optimize several parameters. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and experimental data.

AB - In this paper inverse filtering of transient signals is dealt width. The problem is ill-conditioned, which means that small uncertainty in the measurement causes large deviation 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. The deconvolution algorithms are usually controlled by one ore few number of 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. An algorithm is proposed, based on a spectral model, to optimize several parameters. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and experimental data.

UR - http://www.scopus.com/inward/record.url?scp=0029236768&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029236768&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029236768

SP - 482

EP - 487

BT - Conference Record - IEEE Instrumentation and Measurement Technology Conference

PB - IEEE

ER -