Parameter estimation of superimposed signals by dynamic programming

The problem of fitting a model composed of a number of superimposed signals to noisy data using the maximum-likelihood criterion is considered. A local interaction model is established through the study of Cramer-Rao bound. For such models, the global extremum of the criterion is found efficiently by dynamic programming. An approximate version of the algorithm is developed to further reduce the computation. Using the minimum description length principle, it is shown that the dynamic programming method can be easily adapted to determine the number of signals as well.<
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