Robust filtering for linear systems

1 December 1972

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 11, 652-656
https://doi.org/10.1109/cdc.1972.269093

Abstract

Recursive robust filtering for a discrete, linear stochastic system with additive white noise disturbances is considered. The initial state and plant disturbances are assumed to be Gaussian and the partial covariance of each measurement over a finite region is assumed bounded from below. A soft limiter and patched-Gaussian density are shown to be the optimal min-max estimator and the least favorable measurement density, respectively. An approximate filter is proposed and an example is given.

Keywords

ROBUSTNESS
NONLINEAR FILTERS
FILTERING
LINEAR SYSTEMS
POLLUTION MEASUREMENT
DENSITY MEASUREMENT
HELIUM
STOCHASTIC SYSTEMS
ESTIMATION THEORY
EQUATIONS

This publication has 2 references indexed in Scilit:

Robust Estimation of a Location Parameter
The Annals of Mathematical Statistics, 1964
NON-NORMALITY AND TESTS ON VARIANCES
Biometrika, 1953