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Bayesian Estimation of Class A Noise Parameters with Hidden Channel States

The Middleton Class A interference model is statistical-physical and parametric model for man-made and natural electromagnetic interference. In this letter, the efficient Bayesian estimator of the Class A model parameters is derived and calculated by the Gibbs sampler, a Markov Chain Monte Carlo (MCMC) procedure. The estimator can estimate two-parameter and hidden states for Class A noise model simultaneously. Simulation of this estimator with small sample sizes indicates that this technique is efficient and near-optimal performance.