Subspace method optimized for tracking real-valued sinusoids in noise

Adaptive subspace-based frequency estimation of multiple real valued sine waves is considered in this paper. A novel combination of strong adaptive covariance matrix update and optimized block processing for frequency values retrieval is proposed. The new optimized approach combines the projection approximation subspace tracking technique (for the signal subspace update) with the ESPRIT-like frequency estimation of real-valued sinusoids (for frequency values retrieval). Consequently, a new adaptive subspace-tracking algorithm for frequency estimation is proposed. The algorithm is tested in numerical simulations and compared to complex-valued PAST method. The method proposed brings a significant reduction in arithmetical complexity at the same level of accuracy.