DocumentCode :
891758
Title :
Analysis of identified 2-D noncausal models
Author :
Isaksson, Alf J.
Author_Institution :
Dept. of Signals-Sensors-Syst., R. Inst. of Technol., Stockholm, Sweden
Volume :
39
Issue :
2
fYear :
1993
fDate :
3/1/1993 12:00:00 AM
Firstpage :
525
Lastpage :
534
Abstract :
There are two approaches to the identification of noncausal autoregressive systems in two dimensions differing in the assumed noise model. For both approaches, the maximum likelihood estimator formulated in the frequency domain is presented. The Fisher information matrix is evaluated and found to be the sum of a block-Toeplitz and a block-Hankel matrix. The variance of the parameters, however, cannot be used for comparison of the two approaches, so the variance in the frequency domain is evaluated, assuming that the true system in each case can be described by a model of that type, possibly high-order. In particular, the variance of the spectrum estimate is derived. If the number of parameters tends to infinity, it is shown that the two approaches give the same spectrum estimate variance. The question of which set of true spectra can be described by the respective approaches is discussed
Keywords :
frequency-domain analysis; information theory; matrix algebra; maximum likelihood estimation; parameter estimation; spectral analysis; 2-D noncausal models; Fisher information matrix; MLE; asymptotic analysis; block-Hankel matrix; block-Toeplitz matrix; frequency domain; identification; maximum likelihood estimator; noncausal autoregressive systems; parameter estimation; spectrum estimate variance; spectrum estimation; Covariance matrix; Estimation error; Frequency domain analysis; Frequency estimation; H infinity control; Least squares methods; Maximum likelihood estimation; Parameter estimation; Spectral analysis; System identification;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/18.212282
Filename :
212282
Link To Document :
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