Title :
Small sample properties of the RSS estimation algorithm for Gaussian measurement noise
Author :
Agate, C.S. ; Iltis, R.A.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
Abstract :
The statistics of the reduced sufficient statistics (RSS) estimator are derived for the nonlinear additive white Gaussian noise measurement model. The RSS algorithm recursively propagates a set of sufficient statistics for a mixture density which approximates the true posterior density of a parameter vector. The joint probability density function for the weighting coefficients of the mixture density is derived for the case of additive white Gaussian noise. Through integration of this density, the estimator bias and mean-squared error are determined. The results are applied to a scalar estimation problem in which the sample-averaged statistics are compared to those derived from numerical integration of the density function.
Keywords :
Gaussian noise; error statistics; integration; measurement; parameter estimation; probability; signal sampling; statistical analysis; white noise; AWGN; Gaussian measurement noise; RSS estimation algorithm; estimator bias; joint probability density function; mean-squared error; mixture density; nonlinear additive white Gaussian noise; numerical integration; parameter vector; posterior density; reduced sufficient statistics; sample-averaged statistics; scalar estimation problem; small sample properties; weighting coefficients; Acoustic noise; Additive white noise; Communication networks; Covariance matrix; Density functional theory; Filters; Gaussian noise; Noise measurement; Probability density function; Statistics;
Conference_Titel :
Signals, Systems & Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-8316-3
DOI :
10.1109/ACSSC.1997.679183
