Orthogonal state space decompositions with application to parallel filtering

Author

Rhodes, Ian B. ; Luenberger, Robert A.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA

fYear

1989

fDate

13-15 Dec 1989

Firstpage

2570

Abstract

A necessary and sufficient condition is given for the state space to be decomposable into a direct sum of mutually orthogonal observability subspaces. Such a decomposition has important consequences for the numerical conditioning of the basis changes that are involved in the implementation of an observer or Kalman filter as a collection of parallel subsystems

Keywords

Kalman filters; filtering and prediction theory; observability; state estimation; Kalman filter; mutually orthogonal observability subspaces; observer; orthogonal state space decompositions; parallel filtering; state estimation; Application software; Artificial intelligence; Concurrent computing; Filtering; Matrix decomposition; Observability; State estimation; State-space methods; Sufficient conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1989., Proceedings of the 28th IEEE Conference on

Conference_Location

Tampa, FL

Type

conf

DOI

10.1109/CDC.1989.70641

Filename

70641

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2079331