Model parameter estimation for 2D noncausal Gauss-Markov random fields

Author

Cusani, R. ; Baccarelli, E. ; Galli, S.

Author_Institution

INFOCOM Dept., Rome Univ., Italy

fYear

1995

fDate

17-22 Sep 1995

Firstpage

179

Abstract

An original procedure for estimating the model parameters of a noncausal Gauss-Markov random field (GMRF) from noisy observations is proposed. Starting from a suitable `local´ representation of the field and taking into account the symmetry property of the so-called `potential fields´ describing the GMRF, a linear equation system relating the model parameters to the (generally, nonstationary) 2D autocorrelation function (ACF) of the observed field is derived. Its solution for a known (or estimated) ACF directly gives the parameter estimates of the GMRF. The unknown variance of the eventually present observation noise can be also estimated jointly with the model parameters

Keywords

Gaussian processes; Markov processes; correlation methods; noise; parameter estimation; random processes; 2D noncausal Gauss-Markov random fields; GMRF; linear equation system; local field representation; model parameter estimation; noisy observations; nonstationary 2D autocorrelation function; observation noise; observed field; potential fields; symmetry property; variance; Autocorrelation; Boundary conditions; Equations; Gaussian noise; Gaussian processes; Lattices; Matrices; Parameter estimation; Technological innovation; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on

Conference_Location

Whistler, BC

Print_ISBN

0-7803-2453-6

Type

conf

DOI

10.1109/ISIT.1995.531528

Filename

531528