Vorobyov, S. A. ; Eldar, Y. ; Gershman, A. B. (2006)
Parameter estimation in linear models based on outage probability minimization.
2006 Fortieth Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA (29.10.2006-01.11.2006)
doi: 10.1109/ACSSC.2006.354991
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
A traditional approach to estimating random unknown signal parameters in a noisy linear model aims at minimizing the mean squared error (MSE) averaged over both the random signal parameters and noise realizations. In this paper, we develop a new estimation approach which minimizes the MSE averaged over the noise only. Moreover, in contrast to the traditional approach, the MSE is minimized only for the most favorable signal parameter realizations. It is assumed that the second- order statistics of the unknown signal parameter and noise vectors are precisely known and the noise is Gaussian, while the probability density function (pdf) of the unknown signal parameter vector may be Gaussian or completely unknown. Two different linear estimators are derived for the latter two cases.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2006 |
| Autor(en): | Vorobyov, S. A. ; Eldar, Y. ; Gershman, A. B. |
| Art des Eintrags: | Bibliographie |
| Titel: | Parameter estimation in linear models based on outage probability minimization |
| Sprache: | Englisch |
| Publikationsjahr: | 2006 |
| Ort: | Piscataway |
| Verlag: | IEEE |
| Buchtitel: | 2006 Fortieth Asilomar Conference on Signals, Systems and Computers |
| Veranstaltungstitel: | 2006 Fortieth Asilomar Conference on Signals, Systems and Computers |
| Veranstaltungsort: | Pacific Grove, CA |
| Veranstaltungsdatum: | 29.10.2006-01.11.2006 |
| DOI: | 10.1109/ACSSC.2006.354991 |
| Kurzbeschreibung (Abstract): | A traditional approach to estimating random unknown signal parameters in a noisy linear model aims at minimizing the mean squared error (MSE) averaged over both the random signal parameters and noise realizations. In this paper, we develop a new estimation approach which minimizes the MSE averaged over the noise only. Moreover, in contrast to the traditional approach, the MSE is minimized only for the most favorable signal parameter realizations. It is assumed that the second- order statistics of the unknown signal parameter and noise vectors are precisely known and the noise is Gaussian, while the probability density function (pdf) of the unknown signal parameter vector may be Gaussian or completely unknown. Two different linear estimators are derived for the latter two cases. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik |
| Hinterlegungsdatum: | 20 Nov 2008 08:26 |
| Letzte Änderung: | 21 Nov 2024 09:55 |
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