Details
| Original language | English |
|---|---|
| Title of host publication | 15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings |
| Pages | 1176-1180 |
| Number of pages | 5 |
| Publication status | Published - 2007 |
| Event | 15th European Signal Processing Conference, EUSIPCO 2007 - Poznan, Poland Duration: 3 Sept 2007 → 7 Sept 2007 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| ISSN (Print) | 2219-5491 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
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15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings. 2007. p. 1176-1180 (European Signal Processing Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Kalman filter extension for the analysis of imprecise time series
AU - Neumann, Ingo
AU - Kutterer, Hansjörg
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84863748228&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84863748228
SN - 9788392134022
T3 - European Signal Processing Conference
SP - 1176
EP - 1180
BT - 15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings
T2 - 15th European Signal Processing Conference, EUSIPCO 2007
Y2 - 3 September 2007 through 7 September 2007
ER -