Details
| Original language | English |
|---|---|
| Title of host publication | 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems, QuGOMS’11 - Proceedings of the 2011 IAG International Workshop |
| Editors | Florian Seitz, Hamza Alkhatib, Jeff K.T. Tang, Hansjörg Kutterer, Michael Schmidt |
| Publisher | Springer Verlag |
| Pages | 87-94 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-319-10828-5 |
| ISBN (print) | 978-3-319-10827-8 |
| Publication status | E-pub ahead of print - 3 Nov 2014 |
| Event | 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems, QuGOMS’11: 2011 IAG International Workshop - Munich, Germany Duration: 13 Apr 2011 → 15 Apr 2011 |
Publication series
| Name | International Association of Geodesy Symposia |
|---|---|
| Volume | 140 |
| ISSN (Print) | 0939-9585 |
Abstract
State-space filtering is an important task in geodetic science and in practical applications. The main goal is an optimal combination of prior knowledge about a (non-linear) system and additional information based on observations of the system state. The widely used approach in geodesy is the extended Kalman filter (KF), which minimizes the quadratic error (variance) between the prior knowledge and the observations. The quality of a predicted or filtered system state is only determinable in a reliable way if all significant components of the uncertainty budget are considered and propagated appropriately. But in the nowadays applications, many measurement configurations cannot be optimized to reveal or even eliminate non-stochastic error components.
Therefore, new methods and algorithms are shown to handle these non-stochastic error components (imprecision and fuzziness) in state-space filtering. The combined modeling of random variability and imprecision/fuzziness leads to fuzzy-random variables. In this approach, the random components are modeled in a stochastic framework and imprecision and fuzziness are treated with intervals and fuzzy membership functions. One example in KF is presented which focuses on the determination of a kinematic deformation process in structural monitoring. The results are compared to the pure stochastic case. As the influence of imprecision in comparison to random uncertainty can either be significant or less important during the monitoring process it has to be considered in modeling and analysis.
Keywords
- Fuzziness, Fuzzy random variables, Imprecision, Monitoring, State-space filtering, Uncertainty
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Geophysics
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1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems, QuGOMS’11 - Proceedings of the 2011 IAG International Workshop. ed. / Florian Seitz; Hamza Alkhatib; Jeff K.T. Tang; Hansjörg Kutterer; Michael Schmidt. Springer Verlag, 2014. p. 87-94 (International Association of Geodesy Symposia; Vol. 140).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - State-space Filtering with Respect to Data Imprecision and Fuzziness
AU - Neumann, I.
AU - Kutterer, H.
PY - 2014/11/3
Y1 - 2014/11/3
N2 - State-space filtering is an important task in geodetic science and in practical applications. The main goal is an optimal combination of prior knowledge about a (non-linear) system and additional information based on observations of the system state. The widely used approach in geodesy is the extended Kalman filter (KF), which minimizes the quadratic error (variance) between the prior knowledge and the observations. The quality of a predicted or filtered system state is only determinable in a reliable way if all significant components of the uncertainty budget are considered and propagated appropriately. But in the nowadays applications, many measurement configurations cannot be optimized to reveal or even eliminate non-stochastic error components.Therefore, new methods and algorithms are shown to handle these non-stochastic error components (imprecision and fuzziness) in state-space filtering. The combined modeling of random variability and imprecision/fuzziness leads to fuzzy-random variables. In this approach, the random components are modeled in a stochastic framework and imprecision and fuzziness are treated with intervals and fuzzy membership functions. One example in KF is presented which focuses on the determination of a kinematic deformation process in structural monitoring. The results are compared to the pure stochastic case. As the influence of imprecision in comparison to random uncertainty can either be significant or less important during the monitoring process it has to be considered in modeling and analysis.
AB - State-space filtering is an important task in geodetic science and in practical applications. The main goal is an optimal combination of prior knowledge about a (non-linear) system and additional information based on observations of the system state. The widely used approach in geodesy is the extended Kalman filter (KF), which minimizes the quadratic error (variance) between the prior knowledge and the observations. The quality of a predicted or filtered system state is only determinable in a reliable way if all significant components of the uncertainty budget are considered and propagated appropriately. But in the nowadays applications, many measurement configurations cannot be optimized to reveal or even eliminate non-stochastic error components.Therefore, new methods and algorithms are shown to handle these non-stochastic error components (imprecision and fuzziness) in state-space filtering. The combined modeling of random variability and imprecision/fuzziness leads to fuzzy-random variables. In this approach, the random components are modeled in a stochastic framework and imprecision and fuzziness are treated with intervals and fuzzy membership functions. One example in KF is presented which focuses on the determination of a kinematic deformation process in structural monitoring. The results are compared to the pure stochastic case. As the influence of imprecision in comparison to random uncertainty can either be significant or less important during the monitoring process it has to be considered in modeling and analysis.
KW - Fuzziness
KW - Fuzzy random variables
KW - Imprecision
KW - Monitoring
KW - State-space filtering
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84917698802&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10828-5_13
DO - 10.1007/978-3-319-10828-5_13
M3 - Conference contribution
AN - SCOPUS:84917698802
SN - 978-3-319-10827-8
T3 - International Association of Geodesy Symposia
SP - 87
EP - 94
BT - 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems, QuGOMS’11 - Proceedings of the 2011 IAG International Workshop
A2 - Seitz, Florian
A2 - Alkhatib, Hamza
A2 - Tang, Jeff K.T.
A2 - Kutterer, Hansjörg
A2 - Schmidt, Michael
PB - Springer Verlag
T2 - 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems, QuGOMS’11: 2011 IAG International Workshop
Y2 - 13 April 2011 through 15 April 2011
ER -