Effects of Gaussian perturbations on parameter estimators derived from an estimated signal subspace

Document Type

Conference Proceeding

Date of Original Version



The authors present theoretical analyses that are appropriate for both high and low signal-to-noise ratio (SNR) of signal subspace or SVD-based signal-processing algorithms. For the low-SNR case, the probability of obtaining an outlier is calculated and is used to determine the threshold SNR at which the variance of parameter estimation errors departs from Cramer-Rao bound behavior. At high-SNR, the perturbation of the parameter estimates from SVD-based linear prediction and Prony-Lanczos algorithms is considered using matrix approximation and Taylor series approximation.

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