Statistical analysis of two nonlinear least-squares estimators of sine-wave parameters in the colored-noise case

This paper establishes the large-sample accuracy properties of two nonlinear least-squares estimators (NLSE) of sine-wave parameters: the basic NLSE, which ignores the possible correlation of the noise, and the optimal NLSE, which, besides the sine-wave parameters, also estimates the noise correlation (appropriately parametrized). It is shown that these two NLS estimators have thesame accuracy in large samples. This result provides complete justification for preferring the computationally less expensive basic NLSE over the “optimal” NLSE. Both estimators are shown to achieve the Cramér-Rao Bound (CRB) as the sample size increases. A simple explicit expression for the CRB matrix is provided, which should be useful in studying the performance of sine-wave parameter estimators designed to work in the colored-noise case.

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