A class of fast covariance least squares algorithms

A new class of fast least squares linear prediction algorithms for frequency estimation is described, and a specific example of this class of algorithms is presented in detail. A simulation for the case of single-sinusoid inputs shows that the estimates are quite sensitive to noise for frequencies near half the Nyquist frequency. A simplified error analysis seems to confirm the existence of the noise sensitivity in that range of frequencies.<
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