Data Validation in the Presence of Imprecisely Known Correlations
Abstract:
This paper derives fundamental results for data validation in the presence of imprecisely known correlations. Given a constraint on the maximum absolute correlation of a given estimate and measurement data, a tight upper bound for the joint covariance matrix is derived, which finally yields a modified Mahalanobis distance. The special cases of one-dimensional and two-dimensional random variables are discussed.
| Zugehörige Institution(en) am KIT | Institut für Anthropomatik und Robotik (IAR) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsjahr | 2003 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-9524173-7-9 KITopen-ID: 1000123138 |
| Erschienen in | Proceedings of the 2003 European Control Conference 2003 (ECC 2003), 1-4 September 2003, Cambridge, UK |
| Veranstaltung | European Control Conference (ECC 2003), Cambridge, Vereinigtes Königreich, 01.09.2003 – 04.09.2003 |
| Verlag | Institute of Electrical and Electronics Engineers (IEEE) |
| Externe Relationen | Abstract/Volltext |
| Schlagwörter | Data Validation; Stochastic Uncertainties; Mahalanobis Distance; Imprecisely Known Correlations; Covariance Bounds |
| Nachgewiesen in | Scopus Dimensions OpenAlex |