文章基本信息

标题：Bayesian Segmentation in Signal with Multiplicative Noise Using Reversible Jump Markov Chain Monte Carlo
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作者：Suparman Suparman ; Michel Doisy
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2018
卷号：16
期号：2
页码：673-680
DOI：10.12928/telkomnika.v16i2.7510
语种：English
出版社：Universitas Ahmad Dahlan
其他摘要：This paper proposes the important issues in signal segmentation . The signal is disturbed by multiplicative noise where the number of segments is unknown. A Bayesian approach is proposed to estimate the parameter. T he parameter includes the number of segments, the location of the segment, and the amplitude . T he posterior distribution for the parameter does not have a simple equation so that the Bayes estimator is not easily determined. Reversible Jump M arkov chain M onte C arlo (MCMC) method is adopted to overcome the problem. The Reversible Jump MCMC method creates a Markov chain whose distribution is close to the posterior distribution. The performance of the algorithm is shown by simulation data. T he result of this simulation shows that the algorithm works well. As an application, the algorithm is used to segment a Synthetic Aperture Radar ( SAR ) signal. The advantage of this method is that the number of segments, the position of the segment change, and the amplitude are estimated simultaneously.