Vorobyov, Sergiy ; Eldar, Yonina ; Nemirovski, Arkadi ; Gershman, Alex (2005)
Probability-constrained approach to estimation of random Gaussian parameters.
IEEE CAMSAP 2005 : the First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. Puerto Vallarta, Mexico (13.12.2005-15.12.2005)
doi: 10.1109/CAMAP.2005.1574194
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach can be viewed as a robust generalization of the Wiener filter. It simplifies to the recently proposed robust minimax estimator in some special cases.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2005 |
| Autor(en): | Vorobyov, Sergiy ; Eldar, Yonina ; Nemirovski, Arkadi ; Gershman, Alex |
| Art des Eintrags: | Bibliographie |
| Titel: | Probability-constrained approach to estimation of random Gaussian parameters |
| Sprache: | Englisch |
| Publikationsjahr: | 2005 |
| Ort: | Piscataway, NJ |
| Verlag: | IEEE |
| Buchtitel: | 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005 |
| Veranstaltungstitel: | IEEE CAMSAP 2005 : the First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing |
| Veranstaltungsort: | Puerto Vallarta, Mexico |
| Veranstaltungsdatum: | 13.12.2005-15.12.2005 |
| DOI: | 10.1109/CAMAP.2005.1574194 |
| Kurzbeschreibung (Abstract): | The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach can be viewed as a robust generalization of the Wiener filter. It simplifies to the recently proposed robust minimax estimator in some special cases. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik |
| Hinterlegungsdatum: | 20 Nov 2008 08:21 |
| Letzte Änderung: | 10 Jan 2025 10:52 |
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