Open AccessParameters estimation of correlated non‐Gaussian processes by the method of polynomial maximisation
Author(s) -
Vokorokos Liberios,
Ivchenko Alexander,
Marchevský Stanislav,
Palahina Elena,
Palahin Volodymyr
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0142
Subject(s) - polynomial , mathematics , gaussian , computer science , gaussian process , estimation , algorithm , mathematical optimization , nonlinear system , artificial intelligence , mathematical analysis , chemistry , computational chemistry , physics , quantum mechanics , management , economics
This study is devoted to the development of the new methods and algorithms of the parameter estimation (PE) of correlated non‐Gaussian processes. The modified method of polynomial maximisation and the new models of random processes in the form of higher‐order statistics were used for syntheses of the polynomial algorithms. It is shown that the non‐linear processing of samples, the moment and the cumulant description of correlated non‐Gaussian processes, and taking into account their parameters such as the cumulant coefficients of the third and higher orders can decrease the variance of PE as compared with the well‐known results.