Liu, Tianyi ; Matter, Frederic ; Sorg, Alexander ; Pfetsch, Marc E. ; Haardt, Martin ; Pesavento, Marius (2024)
Maximum A Posteriori Direction-of-Arrival Estimation via Mixed-Integer Semidefinite Programming.
doi: 10.48550/arXiv.2311.03501
Report, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
In this paper, we consider the maximum a posteriori (MAP) estimation for the multiple measurement vectors (MMV) problem with application to direction-of-arrival (DOA) estimation, which is classically formulated as a regularized least-squares (LS) problem with an $\ell_{2,0}$-norm constraint, and derive an equivalent mixed-integer semidefinite program (MISDP) reformulation. The proposed MISDP reformulation can be exactly solved by a generic MISDP solver using a semidefinite programming (SDP) based branch-and-bound method, which, unlike other nonconvex approaches for the MMV problem, such as the greedy methods and sparse Bayesian learning techniques, provides a solution with an optimality assessment even with early termination. We also present an approximate solution approach based on randomized rounding that yields high-quality feasible solutions of the proposed MISDP reformulation at a practically affordable computation time for problems of extremely large dimensions. Numerical simulations demonstrate the improved error performance of our proposed method in comparison to several popular DOA estimation methods. In particular, compared to the deterministic maximum likelihood (DML) estimator, which is often used as a benchmark, the proposed method applied with the randomized rounding algorithm exhibits a superior estimation performance at a significantly reduced running time.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Liu, Tianyi ; Matter, Frederic ; Sorg, Alexander ; Pfetsch, Marc E. ; Haardt, Martin ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Maximum A Posteriori Direction-of-Arrival Estimation via Mixed-Integer Semidefinite Programming |
| Sprache: | Englisch |
| Publikationsjahr: | 17 Oktober 2024 |
| Verlag: | arXiv |
| Reihe: | Signal Processing |
| Auflage: | 2. Version |
| DOI: | 10.48550/arXiv.2311.03501 |
| Kurzbeschreibung (Abstract): | In this paper, we consider the maximum a posteriori (MAP) estimation for the multiple measurement vectors (MMV) problem with application to direction-of-arrival (DOA) estimation, which is classically formulated as a regularized least-squares (LS) problem with an $\ell_{2,0}$-norm constraint, and derive an equivalent mixed-integer semidefinite program (MISDP) reformulation. The proposed MISDP reformulation can be exactly solved by a generic MISDP solver using a semidefinite programming (SDP) based branch-and-bound method, which, unlike other nonconvex approaches for the MMV problem, such as the greedy methods and sparse Bayesian learning techniques, provides a solution with an optimality assessment even with early termination. We also present an approximate solution approach based on randomized rounding that yields high-quality feasible solutions of the proposed MISDP reformulation at a practically affordable computation time for problems of extremely large dimensions. Numerical simulations demonstrate the improved error performance of our proposed method in comparison to several popular DOA estimation methods. In particular, compared to the deterministic maximum likelihood (DML) estimator, which is often used as a benchmark, the proposed method applied with the randomized rounding algorithm exhibits a superior estimation performance at a significantly reduced running time. |
| Zusätzliche Informationen: | New Version of Joint Sparse Estimation with Cardinality Constraint via Mixed-Integer Semidefinite Programming. |
| 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 |
| Hinterlegungsdatum: | 06 Nov 2024 15:31 |
| Letzte Änderung: | 06 Nov 2024 15:31 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
-
Joint Sparse Estimation with Cardinality Constraint via Mixed-Integer Semidefinite Programming. (deposited 14 Nov 2023 14:05)
- Maximum A Posteriori Direction-of-Arrival Estimation via Mixed-Integer Semidefinite Programming. (deposited 06 Nov 2024 15:31) [Gegenwärtig angezeigt]
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