Recursive algorithms for identification of impulse noise channels

The Class A Middleton model is a widely accepted statistical-physical parameteric model for impulsive interference superimposed on a Gaussian background. In the present work, a recursive decision-directed estimator for online identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated and an appropriately modified version is found to yield a recursive algorithm with excellent global performance