Frequency estimation by linear prediction

Abstract

The application of linear prediction to frequency estimation for sinusoidal signals in noise is investigated. It is shown that improved performance is obtained by processing a complex-valued version of the real-valued input signal, with the corresponsing sampling rate reduced by one-half. The case of a single sinusoid in white noise is studied in detail, including the eigenvalues of the covariance matrix, zeros of the inverse filter polynomial, frequency bias, and frequency variance as a function of input SNR and prediction order.