Optimum and efficient algorithms for impulsive channel identification

Abstract

The Class A Middleton noise model is a commonly used, statistical-physical parametric model for impulsive interference superimposed on a Gaussian background. An optimum and practically efficient batch estimator of the Class A parameters is proposed, In particular, this estimator is shown to yield strongly consistent and asymptotically efficient estimates of the Class A parameters. Moreover, via an extensive simulation study, it is seen that the proposed estimator yields excellent estimates of the Class A parameters for moderate sample sizes. >