A Kalman filter extension for the analysis of imprecise time series

verfasst von
Ingo Neumann, Hansjörg Kutterer
Abstract

The Kalman filter combines given physical information for a linear system and external observations of its state in an optimal way. Conventionally, the uncertainty is assessed in a stochastic framework: measurement and system errors are modelled using random variables and probability distributions. However, the quantification of the uncertainty budget of empirical measurements is often too optimistic due to, e.g., the ignorance of non-stochastic errors in the analysis process. For this reason a more general formulation is required which is closer to the situation in real-world applications. Here, the Kalman filter is extended with respect to non-stochastic data imprecision which is caused by hidden systematic errors. The paper presents both the theoretical formulation and a numerical example.

Organisationseinheit(en)
Geodätisches Institut
Typ
Aufsatz in Konferenzband
Seiten
1176-1180
Anzahl der Seiten
5
Publikationsdatum
2007
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Signalverarbeitung, Elektrotechnik und Elektronik
 

Details im Forschungsportal „Research@Leibniz University“