Title :
Multidimensional maximum-entropy covariance extension
Author_Institution :
Stanford University, Stanford, CA
Abstract :
A universal characterization of multi-dimensional maximum-entropy covariances is presented. We show that the maximum-entropy extension of an arbitrary covariance band of a (nonstationary) multi-dimensional signal must have a banded inverse. Furthermore, we show that for one-dimensional signals such banded-inverse covariances are characterized by finite-order autoregressive models. The same kind of model is inadequate for multi-dimensional signals, but it can be used to approximate maximum-entropy covariances.
Keywords :
Covariance matrix; Entropy; Frequency domain analysis; Information systems; Laboratories; Multidimensional systems; Spectral analysis; Stochastic processes; System identification;
Conference_Titel :
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '85.
DOI :
10.1109/ICASSP.1985.1168304