Maximum likelihood estimation of parameters of superimposed signals by using tree-structured EM algorithm

Abstract

As an extension to the conventional EM algorithm, the tree-structured EM (TSEM) algorithm is proposed for the maximumlikelihood (ML) estimation of parameters of superimposed signals. For the special case of superimposed signals in Gaussian noise, the IQML algorithm of Bresler and Macovski is incorporated to the M-step of the EM-based algorithms, resulting in more efficient and reliable maximization. Based on simulations, it is observed that TSEM converges significantly faster than EM, but it is more sensitive to the initial parameter estimates. The hybrid-EM algorithm, which performs a few EM iterations prior to the TSEM iterations, is proposed to capture the desired features of both the EM and TSEM algorithms. Based on simulations, it is found that the hybrid-EM algorithm has significantly more robust convergence than both the EM and TSEM algorithms. © 1998 IEEE.