Numerically robust implementation of multiple-model algorithms

Author

Li, Rong X. ; Zhang, Youmin

Author_Institution

Dept. of Electr. Eng., New Orleans Univ., LA, USA

Volume

36

Issue

1

fYear

2000

fDate

1/1/2000 12:00:00 AM

Firstpage

266

Lastpage

278

Abstract

Standard implementation of multiple-model (MM) estimation algorithms may suffer from numerical problems, especially numerical underflows, which occur when the true model is vastly different from one or more models used in the algorithm. This may be devastating to the performance of the MM algorithm. Numerical robust implementations of some of the most popular MM algorithms are presented. Simulation results are provided to verify the proposed implementation and to compare with the implementations with a lower bound

Keywords

Bayes methods; adaptive estimation; adaptive filters; covariance matrices; probability; sensor fusion; state estimation; target tracking; adaptive approach; covariance; fault detection; generalized pseudo Bayesian estimators; interacting multiple model estimators; jump linear systems; lower bound; model probabilities; multiple-model algorithms; numerical underflows; numerically robust implementation; static multiple model; system modes; target tracking; true model; Adaptive filters; Biomedical signal processing; Fault detection; Power system modeling; Probability; Process control; Robustness; Signal processing algorithms; Target tracking; Uncertainty;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.826329

Filename

826329