EFFICIENT ESTIMATION OF CLASS-A NOISE PARAMETERS VIA THE EM ALGORITHM

The Class A Middleton noise model is a commonly-used statistical-physical, parametric model for non-Gaussian interference superimposed on a Gaussian background. In this study, the problem of efficient estimation of the Class A parameters for small sample sizes is considered. The proposed estimator is based on the EM algorithm, a two-step iterative estimation technique that is ideally suited for the Class A estimation problem since the observations can be readily treated as “incomplete data.” For the single-parameter estimation problem, a closed-form expression for the estimator is obtained. Furthermore, for tne single-parameter estimation problem, it is shown that the sequence of estimates obtained via the EM algorithm converges, and a characterization of the point to which the sequence converges is given. In particular, it is shown that if the limit point of this convergent sequence is an interior point of the parameter set of interest, then it must necessarily be a stationary point of the traditional likelihood function. In addition, for both the single-parameter and two-parameter estimation problems, the small-sample-size performance of the proposed EM algorithm is examined via an extensive simulation study. From this study, it is seen that this likelihood-based scheme yields excellent estimates of the parameters of the Class A model (in terms of attaining the Cramer-Rao lower bound) for small sample sizes. © 1991 IEEE