Robust Spectral Estimation: Autocorrelation Based Minimum Free Energy Method

A new spectral estimation theory is developed which has its conceptual roots in the theory of statistical thermodynamics. The new parametric estimation theory is called minimum free energy (MFE) estimation. Here, in analogy with thermodynamics, a free energy objective function is defined as a linear combination of an error energy and a signal entropy expression. The MFE estimated signal model parameters are commensurate with the global minimum of the free energy function. Statistical analyses of simulations at low SNR's show that MFE is a robust low variance spectral estimator capable of generating high resolution spectral replication from sin- gle snapshot data. The relative merits of MFE are substantiated by simulations comparing MFE with the Tufts-Kurnaresari signal sub- space, noise reduced, modified covariance algorithm.