Conditions of Rice statistical model applicability and estimation of the Rician signal’s parameters by maximum likelihood technique

Yakovleva T.V.
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The paper develops a theory of a new so-called two-parametric approach to the random signals' analysis and processing. A mathematical simulation and the task solutions’ comparison have been implemented for the Gauss and Rice statistical models. The applicability of the Rice statistical model is substantiated for the tasks of data and images processing when the signal’s envelope is being analyzed. A technique is developed and theoretically substantiated for solving the task of the noise suppression and initial image reconstruction by means of joint calculation of both statistical parameters — an initial signal’s mean value and noise dispersion — based on the maximum likelihood method within the Rice distribution. The peculiarities of this distribution’s likelihood function and the following from them possibilities of the signal and noise estimation have been analyzed.

Keywords: random signal, Rice distribution, Gauss distribution, maximum likelihood technique, signal-to-noise ratio
Citation in English: Yakovleva T.V. Conditions of Rice statistical model applicability and estimation of the Rician signal’s parameters by maximum likelihood technique // Computer Research and Modeling, 2014, vol. 6, no. 1, pp. 13-25
Citation in English: Yakovleva T.V. Conditions of Rice statistical model applicability and estimation of the Rician signal’s parameters by maximum likelihood technique // Computer Research and Modeling, 2014, vol. 6, no. 1, pp. 13-25
DOI: 10.20537/2076-7633-2014-6-1-13-25
URL: http://crm-en.ics.org.ru/journal/article/2114/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.
According to Crossref, this article is cited by:
  • Tatiana Victorovna Yakovleva. Analytical solution and computer simulation of the task of Rician distributions parameters in limiting cases of large and small values of signal-to-noise ratio. // Computer Research and Modeling. — 2015. — V. 7, no. 2. — P. 227. DOI: 10.20537/2076-7633-2015-7-2-227-242
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