TU Darmstadt / ULB / TUbiblio

Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays

Liu, Tianyi ; Deram, Sai Pavan ; Ardah, Khaled ; Haardt, Martin ; Pfetsch, Marc E. ; Pesavento, Marius (2024)
Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays.
doi: 10.48550/arXiv.2406.16041
Report, Bibliographie

Kurzbeschreibung (Abstract)

Spatial frequency estimation from a mixture of noisy sinusoids finds applications in various fields. While subspace-based methods offer cost-effective super-resolution parameter estimation, they demand precise array calibration, posing challenges for large antennas. In contrast, sparsity-based approaches outperform subspace methods, especially in scenarios with limited snapshots or correlated sources. This study focuses on direction-of-arrival (DOA) estimation using a partly calibrated rectangular array with fully calibrated subarrays. A gridless sparse formulation leveraging shift invariances in the array is developed, yielding two competitive algorithms under the alternating direction method of multipliers (ADMM) and successive convex approximation frameworks, respectively. Numerical simulations show the superior error performance of our proposed method, particularly in highly correlated scenarios, compared to the conventional subspace-based methods. It is demonstrated that the proposed formulation can also be adopted in the fully calibrated case to improve the robustness of the subspace-based methods to the source correlation. Furthermore, we provide a generalization of the proposed method to a more challenging case where a part of the sensors is unobservable due to failures.

Typ des Eintrags: Report
Erschienen: 2024
Autor(en): Liu, Tianyi ; Deram, Sai Pavan ; Ardah, Khaled ; Haardt, Martin ; Pfetsch, Marc E. ; Pesavento, Marius
Art des Eintrags: Bibliographie
Titel: Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays
Sprache: Englisch
Publikationsjahr: 23 Juni 2024
Verlag: arXiv
Reihe: Signal Processing
Kollation: 16 Seiten
DOI: 10.48550/arXiv.2406.16041
URL / URN: https://arxiv.org/abs/2406.16041
Zugehörige Links:
Kurzbeschreibung (Abstract):

Spatial frequency estimation from a mixture of noisy sinusoids finds applications in various fields. While subspace-based methods offer cost-effective super-resolution parameter estimation, they demand precise array calibration, posing challenges for large antennas. In contrast, sparsity-based approaches outperform subspace methods, especially in scenarios with limited snapshots or correlated sources. This study focuses on direction-of-arrival (DOA) estimation using a partly calibrated rectangular array with fully calibrated subarrays. A gridless sparse formulation leveraging shift invariances in the array is developed, yielding two competitive algorithms under the alternating direction method of multipliers (ADMM) and successive convex approximation frameworks, respectively. Numerical simulations show the superior error performance of our proposed method, particularly in highly correlated scenarios, compared to the conventional subspace-based methods. It is demonstrated that the proposed formulation can also be adopted in the fully calibrated case to improve the robustness of the subspace-based methods to the source correlation. Furthermore, we provide a generalization of the proposed method to a more challenging case where a part of the sensors is unobservable due to failures.

Zusätzliche Informationen:

1. Version

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: 27 Sep 2024 10:29
Letzte Änderung: 19 Dez 2024 11:06
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen