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Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques

Parameter estimation of transient signals, having real decaying exponential constants, is a difficult but important problem that often arises in many areas of scientific disciplines. The frequency domain method of analysis that involves Gardner transformation and conventional inverse filtering often...

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Main Authors: Salami, Momoh Jimoh Emiyoka, Sidek, Shahrul Na'im
Format: Article
Language:English
Published: Elsevier 2003
Subjects:
TA168 Systems engineering
Online Access:http://irep.iium.edu.my/23450/1/signal_journal.pdf
http://irep.iium.edu.my/23450/
http://www.sciencedirect.com/science/article/pii/S0888327002914962
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spelling my.iium.irep.234502012-05-04T08:31:35Z http://irep.iium.edu.my/23450/ Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques Salami, Momoh Jimoh Emiyoka Sidek, Shahrul Na'im TA168 Systems engineering Parameter estimation of transient signals, having real decaying exponential constants, is a difficult but important problem that often arises in many areas of scientific disciplines. The frequency domain method of analysis that involves Gardner transformation and conventional inverse filtering often degrades the quality of deconvolved data, leading to inaccurate results, especially for noisy data. An improved method that is based of the combination of Gardner transformation, optimal compensation deconvolution, and signal modelling techniques is suggested in this paper. In this method of analysis the exponential signal is converted to a convolution model whose input is a train of weighted delta function that contains the signal parameters to be determined.The resolution of the estimated decay rates is poor if the conventional fast Fourier transform (FFT) algorithm is used to analyse the resulting deconvolved data. Using an autoregressive moving (ARMA) model whose AR parameters are determined by solving high-order Yule-Walker equations (HOYWE) via the singular value decomposition (SVD) algorithm can alleviate this shortcoming. The effect of sampling conditions, noise level, number of components and relative sizes of the signal parameters on the performance of this modified method of analysis is examined in this paper. Simulation results show that high resolution estimates of decay constants can be obtained when the above signal processing techniques are used to analyse multi exponential signals with varied signal-to-nose ratio (SNR) . This approach also provides a graphical procedure for detecting and validating the number of exponential signals present in the data. Some computer simulation results are presented to justify the need for this modified method of analysis. Elsevier 2003-11 Article REM application/pdf en http://irep.iium.edu.my/23450/1/signal_journal.pdf Salami, Momoh Jimoh Emiyoka and Sidek, Shahrul Na'im (2003) Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques. Mechanical Systems and Signal Processing, 17 (6). pp. 1201-1218. ISSN 0888-3270 http://www.sciencedirect.com/science/article/pii/S0888327002914962
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TA168 Systems engineering
spellingShingle TA168 Systems engineering
Salami, Momoh Jimoh Emiyoka
Sidek, Shahrul Na'im
Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
description Parameter estimation of transient signals, having real decaying exponential constants, is a difficult but important problem that often arises in many areas of scientific disciplines. The frequency domain method of analysis that involves Gardner transformation and conventional inverse filtering often degrades the quality of deconvolved data, leading to inaccurate results, especially for noisy data. An improved method that is based of the combination of Gardner transformation, optimal compensation deconvolution, and signal modelling techniques is suggested in this paper. In this method of analysis the exponential signal is converted to a convolution model whose input is a train of weighted delta function that contains the signal parameters to be determined.The resolution of the estimated decay rates is poor if the conventional fast Fourier transform (FFT) algorithm is used to analyse the resulting deconvolved data. Using an autoregressive moving (ARMA) model whose AR parameters are determined by solving high-order Yule-Walker equations (HOYWE) via the singular value decomposition (SVD) algorithm can alleviate this shortcoming. The effect of sampling conditions, noise level, number of components and relative sizes of the signal parameters on the performance of this modified method of analysis is examined in this paper. Simulation results show that high resolution estimates of decay constants can be obtained when the above signal processing techniques are used to analyse multi exponential signals with varied signal-to-nose ratio (SNR) . This approach also provides a graphical procedure for detecting and validating the number of exponential signals present in the data. Some computer simulation results are presented to justify the need for this modified method of analysis.
format Article
author Salami, Momoh Jimoh Emiyoka
Sidek, Shahrul Na'im
author_facet Salami, Momoh Jimoh Emiyoka
Sidek, Shahrul Na'im
author_sort Salami, Momoh Jimoh Emiyoka
title Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
title_short Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
title_full Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
title_fullStr Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
title_full_unstemmed Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
title_sort parameter estimation of multicomponent transient signals using deconvolution and arma modelling techniques
publisher Elsevier
publishDate 2003
url http://irep.iium.edu.my/23450/1/signal_journal.pdf
http://irep.iium.edu.my/23450/
http://www.sciencedirect.com/science/article/pii/S0888327002914962
_version_ 1643608587733303296
score 13.211869

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