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  • 标题:Bayesian Segmentation in Signal with Multiplicative Noise Using Reversible Jump Markov Chain Monte Carlo
  • 本地全文:下载
  • 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.
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