A geometric approach to the maximum likelihood spectral estimator for sinusoids in noise

The problem of estimating sinusoids that have been corrupted by additive stationary noise is addressed. It is shown how the Naimark dilation for the data correlation sequence can be used to provide additional insight into some fundamental results on orthogonal polynomials and to give a new interpretation of the maximum-likelihood (ML) spectral estimator.<
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