Tunable line spectral estimators based on state-covariance subspace analysis

Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.