Maximum likelihood estimation of parameters of superimposed signals by using tree-structured EM algorithm
Pakin, Sait Kubilay
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As an extension to the conventional EM algorithm, tree-structured EM algorithm is proposed for the ML estimation of parameters of superimposed signals. For the special case of superimposed signals in Gaussian noise, the IQML algorithm of Breşler 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. 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 Hybrid-EM algorithm has significantly more robust convergence than both the EM and TSEM algorithms.
KeywordsMaximum Likelihood Estimation
Tree-Structured EM Algorithm