Maximum likelihood identification of correlation matrices for estimation of power spectra at arbitrary resolutions
- 24 March 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 12, 2066-2069
- https://doi.org/10.1109/icassp.1987.1169390
Abstract
In spectral estimation spectra are usually derived from an AR or MA model fitted to the data. An implicit step common to these methods is the estimation of the correlation matrix. In this paper our approach consists in doing maximum likelihood identification of structured correlation matrix. We have used two different structures corresponding to Toeplitz matrices and to matrices with DFT representation. Both structures are related to time invariant series. We have studied the performances of the spectral estimates obtained from our correlation matrix. In particular we show mean square error versus SNR plots for the frequency estimation of two noisy sinusoids. Author(s) Tourtier, P. University of Colorado, Boulder, CO Scharf, L.Keywords
This publication has 3 references indexed in Scilit:
- Computation of the exact information matrix of Gaussian time series with stationary random componentsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihoodProceedings of the IEEE, 1982
- Estimation of structured covariance matricesProceedings of the IEEE, 1982