Statistics of the RSS estimation algorithm for Gaussian measurement noise

The large and small sample properties of the reduced sufficient statistics (RSS) estimator of Kulhavy (1990, 1992) 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 that approxi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 1999-01, Vol.47 (1), p.22-32
Main Authors: Agate, C.S., Iltis, R.A.
Format: Article
Language:English
Subjects:
Additive white noise
Algorithms
Applied sciences
Bias
Covariance matrix
Density
Density functional theory
Density measurement
Estimators
Exact sciences and technology
Filters
Gaussian
Gaussian noise
Information, signal and communications theory
Mathematical analysis
Mathematical methods
Mathematical models
Noise measurement
Noise reduction
Probability density function
Statistics
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The large and small sample properties of the reduced sufficient statistics (RSS) estimator of Kulhavy (1990, 1992) 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 that 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 phase estimation problem in which the sample-averaged statistics are compared with those derived from numerical integration of the density function. The asymptotic bias and variance of the RSS estimator are also derived and compared with simulation results.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.738236