Effects of Gaussian perturbations on parameter estimators derived from an estimated signal subspace
Document Type
Conference Proceeding
Date of Original Version
12-1-1988
Abstract
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.
Citation/Publisher Attribution
Kot, A. C., C. D. Melissinos, D. W. Tufts, and R. J. Vaccaro. "Effects of Gaussian perturbations on parameter estimators derived from an estimated signal subspace." (1988): 86-91. https://digitalcommons.uri.edu/ele_facpubs/1190