Abstract
his paper considers the problem of estimating the parameters of two-dimensional (2-D) moving average random (MA) fields. We first address the problem of expressing the covariance matrix of nonsymmetrical half-plane, noncausal, and quarter-plane MA random fields in terms of the model parameters. Assuming the random field is Gaussian, we derive a closedform expression for the Cramér-Rao lower bound (CRLB) on the error variance in jointly estimating the model parameters. A computationally efficient algorithm for estimating the parameters of the MA model is developed. The algorithm initially fits a 2-D autoregressive model to the observed field and then uses the estimated parameters to compute the MA model. A maximumlikelihood algorithm for estimating the MA model parameters is also presented. The performance of the proposed algorithms is illustrated by Monte-Carlo simulations and is compared with the Cramér-Rao bound.
Original language | English |
---|---|
Pages (from-to) | 2157-2165 |
Number of pages | 9 |
Journal | IEEE Transactions on Signal Processing |
Volume | 46 |
Issue number | 8 |
DOIs | |
State | Published - 1 Dec 1998 |
Keywords
- Maximum likelihood
- Moving average random fields
- Parameter estimation
- Random fields
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering