Nonparametric estimation of a mixture of probability densities of signals (interferences)

In this article, we suggest using the parallel clustering procedure for the nonparametric estimation of a mixture of probability densities of signals (interferences). This procedure makes it possible to estimate with an adequate accuracy the parameters of the marginal mixture distribution, as well as the number of components (classes) in a mixture. The efficiency of the suggested method is confirmed by the numerical results.