New algorithms for the identification of impulsive noise

Summary form only given, as follows. A physically meaningful and widely accepted parametric model for impulsive interference is the so-called Class A Middleton model, whose parameters A, Γ, and σ² can be chosen to give accurate models of a wide range of non-Gaussian channels. The quantity A, referred to as the overlap index, is a measure of the average overlap of successive emissions; Γ, the Gaussian factor, is the ratio of the intensity of the independent Gaussian component of the input interference to that of the non-Gaussian component; and σ² is simply the second moment of the interference envelope. In this study, a recursive decision-directed estimator of the patterns 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, the second moment of the impulsive component of the interference envelope, and the probability with which the impulses occur are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly.