Multidimensional Digital Signal Processing
Multidimensional Digital Signal Processing
IEEE Transactions on Signal Processing
Parameter estimation of two-dimensional moving average randomfields
IEEE Transactions on Signal Processing
Cramer-Rao bound on the estimation accuracy of complex-valuedhomogeneous Gaussian random fields
IEEE Transactions on Signal Processing
A unified texture model based on a 2-D Wold-like decomposition
IEEE Transactions on Signal Processing
Two-dimensional orthogonal lattice structures for autoregressivemodeling of random fields
IEEE Transactions on Signal Processing
Optimum asymmetric half-plane autoregressive lattice parameter modeling of 2-D fields
IEEE Transactions on Signal Processing
Characterization and estimation of two-dimensional ARMA models
IEEE Transactions on Information Theory
Maximum likelihood parameter estimation of textures using a Wold-decomposition based model
IEEE Transactions on Image Processing
On 2-D recursive LMS algorithms using ARMA prediction for ADPCM encoding of images
IEEE Transactions on Image Processing
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The Cramer-Rao lower bound (CRLB) that gives the minimal achievable variance/standard deviation for any unbiased estimator offers a useful tool for an assessment of the consistency of parameter estimation techniques. In this paper, a closed-form expression for the computation of the exact CRLB on unbiased estimates of the parameters of a two-dimensional (2-D) autoregressive moving average (ARMA) model with a nonsymmetric half-plane (NSHP) region of support is developed. The proposed formulation is mainly based on a matrix representation of 2-D real-valued discrete and homogeneous random field characterized by the NSHP ARMA model. Assuming that the random field is Gaussian, the covariance matrix of the NSHP ARMA random field is first expressed in terms of the model parameters. Then, using this matrix structure, a closed-form expression of the exact Fisher information matrix required for the CRLB computation of the NSHP ARMA model parameters is developed. Finally, the main formulas derived for the NSHP ARMA model are rearranged for its autoregressive and moving average counterparts, separately. Numerical simulations are included to demonstrate the behavior of the derived CRLB formulas.