Abstract:

Atmospheric noise is the main interference in a low-frequency communication system, which is highly impulsive. So the work for estimating the parameters of model of non-Gaussian noises is of great significance to improve the performance of the low-frequency receiver. This paper proposes a Markov chain Monte Carlo (MCMC) method to estimate the parameters of a mixture model. The method updates the parameters through a Gibbs sampler and M-H algorithm, which are based on the Bayesian hierarchical model. The α stable distribution in the mixture model is equivalent to the normal distribution by using the product properties. An extra layer is added to the hierarchy for full flexibility. The result shows that the new method has a good performance, high precision and can be excellently applied in practice.