Estimation of structured covariance matrices and multiple window spectrum analysis Van Veen, Barry ; Scharf, Louis L. "This work was supported in part by the Wisconsin Alumni Research Foundation, the National Science Foundation under MIP-8958559, and by the Office of Naval Research, Statistics and Probability Branch, under Contract N00014-85-K-0256." An intimate relationship between low rank modeling and multiple window spectrum estimation is demonstrated by using maximum likelihood estimates of structured covariance matrices. The power in a narrow spectral band is estimated by estimating the variances in a low rank signal plus noise covariance model. This model is swept through the entire frequency band to obtain an estimate of power as a function of frequency. The resulting spectrum estimates are given by weighted combinations of eigenspectra. Each eigenspectrum results from projecting the data onto an orthogonal component of the signal subspace and squaring. The multiple window spectrum estimates of Thomson correspond to a particular choice for the low rank signal model. The low rank modeling and structured covariance matrix framework is also used to derive the maximum likelihood estimate for the center frequency of a signal in noise. This estimate is also obtained from a weighted combination of eigenspectra. Colorado State University. Libraries 1990 text ; image application/pdf ECElls00003.pdf FACFECEN100392ARTI eng c1990 IEEE
Estimation of structured covariance matrices and multiple window spectrum analysis
Van Veen, Barry ; Scharf, Louis L.
"This work was supported in part by the Wisconsin Alumni Research Foundation, the National Science Foundation under MIP-8958559, and by the Office of Naval Research, Statistics and Probability Branch, under Contract N00014-85-K-0256."
An intimate relationship between low rank modeling and multiple window spectrum estimation is demonstrated by using maximum likelihood estimates of structured covariance matrices. The power in a narrow spectral band is estimated by estimating the variances in a low rank signal plus noise covariance model. This model is swept through the entire frequency band to obtain an estimate of power as a function of frequency. The resulting spectrum estimates are given by weighted combinations of eigenspectra. Each eigenspectrum results from projecting the data onto an orthogonal component of the signal subspace and squaring. The multiple window spectrum estimates of Thomson correspond to a particular choice for the low rank signal model. The low rank modeling and structured covariance matrix framework is also used to derive the maximum likelihood estimate for the center frequency of a signal in noise. This estimate is also obtained from a weighted combination of eigenspectra.
Colorado State University. Libraries
1990
text ; image
application/pdf
ECElls00003.pdf
FACFECEN100392ARTI
eng
c1990 IEEE