Reinhard, Dominik ; Fauß, Michael ; Zoubir, Abdelhak M. (2024)
Asymptotically Optimal Procedures for Sequential Joint Detection and Estimation.
In: Signal Processing, 219
doi: 10.1016/j.sigpro.2024.109410
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is validated by numerical examples.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Reinhard, Dominik ; Fauß, Michael ; Zoubir, Abdelhak M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Asymptotically Optimal Procedures for Sequential Joint Detection and Estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 1 Juni 2024 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Signal Processing |
| Jahrgang/Volume einer Zeitschrift: | 219 |
| DOI: | 10.1016/j.sigpro.2024.109410 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is validated by numerical examples. |
| Zusätzliche Informationen: | Art.No.:109410 |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung |
| Hinterlegungsdatum: | 14 Mai 2021 06:07 |
| Letzte Änderung: | 23 Feb 2024 07:55 |
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