Stochastic algorithms for marginal MAP retrieval of sinusoids in non-Gaussian noise
In this paper we propose a method to estimate the frequencies of sinusoids embedded in non-Gaussian noise. We model the noise using mixtures of Gaussians and propose two original, efficient algorithms that allow for the marginal maximum a posteriori (MAP) estimation of the sinusoid parameters to be estimated. Outline of the proof of convergence of the algorithms is also given and simulation results are presented.