Title :
Multi-dimensional power spectrum estimation using noncausal rational models
Author :
Arun, K.S. ; Krogmeier, J.V.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA
Abstract :
Methods are presented for the identification of noncausal, rational, multidimensional systems from covariance data in connection with the development of noncausal models in multidimensional power spectrum estimation. It is shown how a recently proposed notion of state for noncausal systems and the resulting rank properties can be used for model estimation. The general class of noncausal systems studied encompasses the quarter-plane causal, all-pole, separable, and factorizable models previously considered for 2-D spectrum estimation
Keywords :
identification; multidimensional systems; signal processing; spectral analysis; covariance data; identification; model estimation; multidimensional power spectrum estimation; multidimensional systems; noncausal rational models; rank properties; Approximation algorithms; Covariance matrix; Multidimensional systems; Power system modeling; Predictive models; Signal processing; Spectral analysis; Stability; Technological innovation; White noise;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on
Conference_Location :
New York, NY
DOI :
10.1109/ICASSP.1988.196689
