Open AccessPerformance Analysis of Gibbs Sampling for Bayesian Extracting Sinusoids
Author(s) -
Mehmet Cevri,
Dursun Üstündağ
Publication year - 2021
Publication title -
international journal of mathematical models and methods in applied sciences
Language(s) - English
Resource type - Journals
ISSN - 1998-0140
DOI - 10.46300/9101.2021.15.19
Subject(s) - gibbs sampling , estimator , cramér–rao bound , bayesian probability , sampling (signal processing) , algorithm , white noise , statistics , mathematics , fourier transform , signal to noise ratio (imaging) , computer science , pattern recognition (psychology) , artificial intelligence , telecommunications , mathematical analysis , detector
This paper involves problems of estimating parameters of sinusoids from white noisy data by using Gibbs sampling (GS) in a Bayesian framework. Modifications of its algorithm is tested on data generated from synthetic signals and its performance is compared with conventional estimators such as Maximum Likelihood(ML) and Discrete Fourier Transform (DFT) under a variety of signal to noise ratio (SNR) and different length of data sampling (N), regarding to Cramér-Rao lower bound (CRLB). All simulation results show its effectiveness in frequency and amplitude estimation of sinusoids.