EM algorithm convergence for inertial navigation system alignment

Author

Einicke, Garry A.

Author_Institution

Commonwealth Sci. & Ind. Res. Organ. (CSIRO), Pullenvale, QLD, Australia

fYear

2008

fDate

9-11 Dec. 2008

Firstpage

1310

Lastpage

1314

Abstract

The convergence of a Kalman filter-based EM algorithm for estimating variances is investigated. It is established that if the variance estimates and the error covariances are initialized appropriately, the sequence of variance iterates will be monotonically nonincreasing. Under prescribed conditions, the variance estimates will converge to the actual values. An inertial navigation application is discussed in which performance depends on accurately estimating the process variances.

Keywords

Kalman filters; convergence of numerical methods; covariance analysis; error analysis; expectation-maximisation algorithm; inertial navigation; Kalman filter-based EM algorithm convergence; error covariance; expectation-maximisation algorithm; inertial navigation system alignment; variance estimation; variance iterate sequence; Control systems; Convergence; Difference equations; Inertial navigation; Iterative algorithms; Maximum likelihood estimation; Noise measurement; Parameter estimation; Riccati equations; State estimation; Kalman filtering; inertial navigation; parameter estimation; stationary alignment;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2008. CDC 2008. 47th IEEE Conference on

Conference_Location

Cancun

ISSN

0191-2216

Print_ISBN

978-1-4244-3123-6

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2008.4739113

Filename

4739113