Expectation and maximization algorithm for estimating parameters of a simple partial erasure model

The identification of the model parameters of a high-density recording channel generally requires solution of nonlinear equations. In this paper, we apply the expectation and maximization (EM) algorithm to realize the maximum likelihood estimation of the parameters of a simple partial erasure model, including the reduction parameters and the isolated transition response. The algorithm that results from this approach iteratively solves two least-squares problems and, thus, realization is simple. Computer simulations verify the feasibility of the EM algorithm, and show that the proposed algorithm has fast convergence and the resulting estimator is asymptotically efficient. Index Terms--Expectation and maximization algorithm, least-squares methods, maximum-likelihood estimation, Monte Carlo methods, simple partial erasure model.