Estimation of Non-Gaussian Noise Parameters Using Markov Chain Monte Carlo Method
XU Da-yong · 2007
A fast convergence Bayesian estimator of the class A model parameters is derived and calculated using the Markov Chain Monte Carlo(MCMC) procedure.This estimator can estimate the impulsive index,Gauss-to-impulsive power ratio,noise power,and hidden states of class A noise model for the channel simultaneously.The considered estimator is different from traditional estimator,which provides a novel method with low-complexity,global optimization capability and potential for parallel processing.Simulation with small sample sizes shows effectiveness of the technique.