The algebraic inversion of 2-D autoregressive power spectra with applications to spectral estimation
- 24 March 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 7, 133-135
- https://doi.org/10.1109/icassp.1982.1171698
Abstract
Some recent results concerning the evaluation of autocorrelation functions associated with 2-D autoregressive (AR) spectra are reviewed. In contrast to the 1-D case, 2-D AR autocorrelation functions can, in general, only be evaluated by means of a numerical integration. However, if the minimum-phase whitening filter for the AR process has finite reflection coefficient support, then the autocorrelation function can be evaluated algebraically, either by means of a "backward" 2-D Levinson algorithm, or by means of a partial fraction expansion. The special properties of 2-D AR spectra of this class make them potentially useful in the problem of finding correlation-matching spectral estimates. The possibility of performing 2-D covariance extension by fitting AR models with finite reflection coefficient Support iS partially explored.Keywords
This publication has 3 references indexed in Scilit:
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- A note on the 2-D partial fraction expansionIEEE Transactions on Circuits and Systems, 1980