Performance comparisons of the minimum free energy algorithms with the reduced rank modified covariance eigenanalysis algorithm

Low SNR simulations comparing the minimum-free-energy (MFE) spectral estimation algorithms of Silverstein and Pimbley (1988) with the reduced rank modified covariance eigenanalysis algorithm of Tufts and Kumaresan (1982) have been performed. Two different MFE algorithms are discussed and simulated. The results of the statistical analyses demonstrate that both MFE algorithms are robust, low-variance spectral estimators capable of making reliable frequency estimations of closely spaced sources at very low SNRs, from single snapshot data. They are applicable to the general domain of spectral estimation, while the eigenanalysis algorithms are primarily restricted to line spectra frequency estimation. Nonetheless, for simulations of closely spaced line spectra at low SNRs, the MFE estimations are considerably more robust than the Tufts-Kumaresan estimations