Title :
Joint Bayesian detection and estimation of sinusoids embedded in noise
Author :
Andrieu, Christophe ; Doucet, Arnaud ; Duvant, P.
Author_Institution :
CNRS, Cergy, France
Abstract :
In this paper we address the problem of the joint detection and estimation of sinusoids embedded in noise, from a Bayesian point of view. We first propose an original Bayesian model. A large number of parameters has to be estimated, including the number of sinusoids. No analytical developments can be performed. This leads us to design a new stochastic algorithm relying on reversible jump MCMC (Markov chain Monte Carlo). We obtain very satisfactory results
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; noise; parameter estimation; signal detection; Markov chain Monte Carlo; joint Bayesian detection estimation; reversible jump MCMC; sinusoids; stochastic algorithm; Algorithm design and analysis; Bayesian methods; Gaussian noise; Integrated circuit modeling; Integrated circuit noise; Performance analysis; Probability distribution; Signal processing algorithms; Stochastic processes; Stochastic resonance;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.681595
