Liu, Tianyi ; Matter, Frederic ; Sorg, Alexander ; Pfetsch, Marc E. ; Haardt, Martin ; Pesavento, Marius (2023)
Joint Sparse Estimation with Cardinality Constraint via Mixed-Integer Semidefinite Programming.
9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. Herradura, Costa Rica (10.-13.12.2023)
doi: 10.1109/CAMSAP58249.2023.10403415
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The multiple measurement vectors (MMV) problem refers to the joint estimation of multiple signal realizations where the signal samples share a common sparse support over a known dictionary, which is a fundamental challenge in various applications in signal processing, e.g., direction-of-arrival (DOA) estimation. We consider the maximum a posteriori (MAP) estimation of an MMV problem, which is classically formulated as a regularized least-squares (LS) problem with an ℓ2,0 -norm constraint and derive an equivalent mixed-integer semidefinite program (MISDP) reformulation, which can be solved by state-of-the-art numerical MISDP solvers at an affordable computation time. Numerical simulations in the context of DOA estimation demonstrate the improved error performance of our proposed method in comparison to several popular DOA estimation methods.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Liu, Tianyi ; Matter, Frederic ; Sorg, Alexander ; Pfetsch, Marc E. ; Haardt, Martin ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Joint Sparse Estimation with Cardinality Constraint via Mixed-Integer Semidefinite Programming |
| Sprache: | Englisch |
| Publikationsjahr: | 14 Dezember 2023 |
| Verlag: | IEEE |
| Buchtitel: | 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP |
| Veranstaltungstitel: | 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing |
| Veranstaltungsort: | Herradura, Costa Rica |
| Veranstaltungsdatum: | 10.-13.12.2023 |
| DOI: | 10.1109/CAMSAP58249.2023.10403415 |
| Kurzbeschreibung (Abstract): | The multiple measurement vectors (MMV) problem refers to the joint estimation of multiple signal realizations where the signal samples share a common sparse support over a known dictionary, which is a fundamental challenge in various applications in signal processing, e.g., direction-of-arrival (DOA) estimation. We consider the maximum a posteriori (MAP) estimation of an MMV problem, which is classically formulated as a regularized least-squares (LS) problem with an ℓ2,0 -norm constraint and derive an equivalent mixed-integer semidefinite program (MISDP) reformulation, which can be solved by state-of-the-art numerical MISDP solvers at an affordable computation time. Numerical simulations in the context of DOA estimation demonstrate the improved error performance of our proposed method in comparison to several popular DOA estimation methods. |
| Freie Schlagworte: | Maximum a posteriori estimation, Direction-of-arrival estimation, Dictionaries, Estimation, Signal processing, Numerical simulation, Time measurement, DOA estimation, multiple measurement vectors, joint sparsity, mixed-integer semidefinite program, maximum a posteriori estimation |
| 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 > Nachrichtentechnische Systeme 04 Fachbereich Mathematik 04 Fachbereich Mathematik > Optimierung 04 Fachbereich Mathematik > Optimierung > Discrete Optimization |
| TU-Projekte: | DFG|PE2080/2-1|Der Partielle Relaxa |
| Hinterlegungsdatum: | 11 Apr 2024 12:05 |
| Letzte Änderung: | 11 Apr 2024 12:05 |
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