Novel Subspace Approach for Efficient Sinusoidal Parameter Estimation

Parameter estimation of sinusoids from a finite number of noisy discrete-time measurements is an important topic in science and engineering because numerous real-world signals can be well described by the sinusoidal model. Although many spectral estimation methods are available in the literature, there is generally a tradeoff between estimation performance and computational requirement. The aim of this research is to develop subspace-based sinusoidal parameter estimation approaches that are efficient in both aspects of accuracy and complexity. To fulfill these challenging demands, we suggest to effectively utilize the principal singular vectors determined from the observed signals. Moreover, we will extend the proposed methodology for estimating the number of sinusoids in case it is unknown. Performance analysis of the devised spectral estimators will also be derived.