Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC
Piecewise constant models have been used in signal processing. The signal contains noise so noise needs to be eliminated. Several research results have used the assumption that noise has a normal, gamma, or Laplace distribution. However, the signal may have noise with other distributions. This study...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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GEOMATE International Society
2022
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| Online Access: | http://eprints.utm.my/101211/1/AsnulDaharMinghat2022_BayesianDetectionofSignalUnderRayleigh.pdf |
| _version_ | 1796867021672022016 |
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| author | Suparman, Suparman Toifur, Mohammad Minghat, Asnul Dahar Hikamudin, Eviana Rusiman, Mohd. Saifullah |
| author_facet | Suparman, Suparman Toifur, Mohammad Minghat, Asnul Dahar Hikamudin, Eviana Rusiman, Mohd. Saifullah |
| author_sort | Suparman, Suparman |
| collection | ePrints |
| description | Piecewise constant models have been used in signal processing. The signal contains noise so noise needs to be eliminated. Several research results have used the assumption that noise has a normal, gamma, or Laplace distribution. However, the signal may have noise with other distributions. This study aims to propose a piecewise-constant model in which noise is assumed to have a Rayleigh distribution. This study also proposes a method for estimating the parameters of a piecewise-constant model that contains Rayleigh noise. The parameters of the piecewise constant model were estimated in the Bayesian framework by adopting the reversible jump Markov Chain Monte Carlo (MCMC) method. This research shows that the dimension of the parameter space is a combination of several spaces with different dimensions. Bayes estimators for the parameters of the piecewise constant model cannot be stated explicitly. The reversible jump MCMC method is used to calculate the Bayes estimator. The results of this study have a significant contribution in providing Rayleigh noise as an alternative noise in signal processing. This research has a novelty, namely: the use of Rayleigh noise in the piecewise constant model and the hierarchical Bayesian procedure to estimate the parameters of the piecewise constant model. Further research can be extended to the estimation procedure of the piecewise constant with Weibull noise. |
| first_indexed | 2024-03-05T21:20:56Z |
| format | Article |
| id | utm.eprints-101211 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:20:56Z |
| publishDate | 2022 |
| publisher | GEOMATE International Society |
| record_format | dspace |
| spelling | utm.eprints-1012112023-06-08T08:15:20Z http://eprints.utm.my/101211/ Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC Suparman, Suparman Toifur, Mohammad Minghat, Asnul Dahar Hikamudin, Eviana Rusiman, Mohd. Saifullah T Technology (General) Piecewise constant models have been used in signal processing. The signal contains noise so noise needs to be eliminated. Several research results have used the assumption that noise has a normal, gamma, or Laplace distribution. However, the signal may have noise with other distributions. This study aims to propose a piecewise-constant model in which noise is assumed to have a Rayleigh distribution. This study also proposes a method for estimating the parameters of a piecewise-constant model that contains Rayleigh noise. The parameters of the piecewise constant model were estimated in the Bayesian framework by adopting the reversible jump Markov Chain Monte Carlo (MCMC) method. This research shows that the dimension of the parameter space is a combination of several spaces with different dimensions. Bayes estimators for the parameters of the piecewise constant model cannot be stated explicitly. The reversible jump MCMC method is used to calculate the Bayes estimator. The results of this study have a significant contribution in providing Rayleigh noise as an alternative noise in signal processing. This research has a novelty, namely: the use of Rayleigh noise in the piecewise constant model and the hierarchical Bayesian procedure to estimate the parameters of the piecewise constant model. Further research can be extended to the estimation procedure of the piecewise constant with Weibull noise. GEOMATE International Society 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/101211/1/AsnulDaharMinghat2022_BayesianDetectionofSignalUnderRayleigh.pdf Suparman, Suparman and Toifur, Mohammad and Minghat, Asnul Dahar and Hikamudin, Eviana and Rusiman, Mohd. Saifullah (2022) Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC. International Journal of GEOMATE, 22 (89). pp. 24-31. ISSN 2186-2982 http://dx.doi.org/10.21660/2022.89.7599 DOI: 10.21660/2022.89.7599 |
| spellingShingle | T Technology (General) Suparman, Suparman Toifur, Mohammad Minghat, Asnul Dahar Hikamudin, Eviana Rusiman, Mohd. Saifullah Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC |
| title | Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC |
| title_full | Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC |
| title_fullStr | Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC |
| title_full_unstemmed | Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC |
| title_short | Bayesian detection of signal under Rayleigh multiplicative noise based on reversible jump MCMC |
| title_sort | bayesian detection of signal under rayleigh multiplicative noise based on reversible jump mcmc |
| topic | T Technology (General) |
| url | http://eprints.utm.my/101211/1/AsnulDaharMinghat2022_BayesianDetectionofSignalUnderRayleigh.pdf |
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