Dynamic cluster algorithm for Gaussian mixture parameter estimation

Probability density of non-Gaussian processes can be well fit by Gaussian mixture model whose parameters can be estimated through the dynamic clutter algorithm that is a recursion on the principle of minimum mean-square deviation.All possible boundaries of each clutter are forward-derived.Since the right boundary of final clutter is determinate,boundaries of previous clutters can be recurred backwards one by one.Thus the Gaussian mixture parameters are estimated.After descriptions of the model and the estimation problem,dynamic clutter algorithm for Gaussian mixture parameters is obtained.Its essential ideas and applicability are discussed in detail.A numerical example is presented to study the performance of estimation.