Trinh Hoang, Minh (2020)
Partial Relaxation: A Computationally Efficient Direction-of-Arrival Estimation Framework.
Technische Universität Darmstadt
doi: 10.25534/tuprints-00011767
Dissertation, Erstveröffentlichung
Kurzbeschreibung (Abstract)
Direction-of-Arrival (DOA) estimation from data collected at a sensor array in the presence of noise has been a fundamental and long-established research topic of interest in sensor array processing. The application of DOA estimation does not only restrict to radar but also spans multiple additional fields of research, including radio astronomy, biomedical imaging, seismic exploration, wireless communication, among others.
Due to the wide applications of DOA estimation, various methods have been developed in the literature to increase the resolution capability, computational efficiency, and robustness of the algorithms. However, a trade-off between the estimation performance and the computational complexity is generally inevitable. This thesis addresses the challenge of developing low-complexity DOA estimators with the ability to resolve closely spaced source signals in the threshold region, i.e., low sample size or low Signal-to-Noise ratio.
Motivated by various interpretations of the conventional DOA estimators in the literature and their implied signal models, in this thesis, we introduce a novel class of DOA estimators which is referred to as the Partial Relaxation framework. In the Partial Relaxation framework, the DOA parameters are estimated from the eigenvalues of a particular modified covariance matrix at each look-direction in the Field-of-View. Simulations show that the proposed DOA estimators achieve excellent performance in the threshold region without exploiting any particular structure of the sensor array.
Theoretical and practical aspects of the DOA estimators under the proposed framework are investigated in this thesis. From the practical perspective, as the computation of selected eigenvalues is crucial for the proposed estimators, we introduce a general and efficient implementation which exploits the underlying structure of the matrix argument induced by the Partial Relaxation framework. Compared with the naive implementation, the execution time of the proposed estimators with the efficient implementation is reduced by multiple orders of magnitude. From the theoretical aspect, a closed-form expression of the lower bound for the estimation error of all unbiased estimators under the Partial Relaxation framework is derived. Consequently, implications and comparisons of the proposed lower bound with theoretical results in the literature are studied and discussed.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2020 | ||||
Autor(en): | Trinh Hoang, Minh | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Partial Relaxation: A Computationally Efficient Direction-of-Arrival Estimation Framework | ||||
Sprache: | Englisch | ||||
Referenten: | Pesavento, Prof. Dr. Marius ; Viberg, Prof. Dr. Mats | ||||
Publikationsjahr: | 2020 | ||||
Ort: | Darmstadt | ||||
Datum der mündlichen Prüfung: | 30 April 2020 | ||||
DOI: | 10.25534/tuprints-00011767 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/11767 | ||||
Kurzbeschreibung (Abstract): | Direction-of-Arrival (DOA) estimation from data collected at a sensor array in the presence of noise has been a fundamental and long-established research topic of interest in sensor array processing. The application of DOA estimation does not only restrict to radar but also spans multiple additional fields of research, including radio astronomy, biomedical imaging, seismic exploration, wireless communication, among others. Due to the wide applications of DOA estimation, various methods have been developed in the literature to increase the resolution capability, computational efficiency, and robustness of the algorithms. However, a trade-off between the estimation performance and the computational complexity is generally inevitable. This thesis addresses the challenge of developing low-complexity DOA estimators with the ability to resolve closely spaced source signals in the threshold region, i.e., low sample size or low Signal-to-Noise ratio. Motivated by various interpretations of the conventional DOA estimators in the literature and their implied signal models, in this thesis, we introduce a novel class of DOA estimators which is referred to as the Partial Relaxation framework. In the Partial Relaxation framework, the DOA parameters are estimated from the eigenvalues of a particular modified covariance matrix at each look-direction in the Field-of-View. Simulations show that the proposed DOA estimators achieve excellent performance in the threshold region without exploiting any particular structure of the sensor array. Theoretical and practical aspects of the DOA estimators under the proposed framework are investigated in this thesis. From the practical perspective, as the computation of selected eigenvalues is crucial for the proposed estimators, we introduce a general and efficient implementation which exploits the underlying structure of the matrix argument induced by the Partial Relaxation framework. Compared with the naive implementation, the execution time of the proposed estimators with the efficient implementation is reduced by multiple orders of magnitude. From the theoretical aspect, a closed-form expression of the lower bound for the estimation error of all unbiased estimators under the Partial Relaxation framework is derived. Consequently, implications and comparisons of the proposed lower bound with theoretical results in the literature are studied and discussed. |
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URN: | urn:nbn:de:tuda-tuprints-117679 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau | ||||
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 |
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Hinterlegungsdatum: | 01 Jul 2020 13:16 | ||||
Letzte Änderung: | 07 Jul 2020 07:18 | ||||
PPN: | |||||
Referenten: | Pesavento, Prof. Dr. Marius ; Viberg, Prof. Dr. Mats | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 30 April 2020 | ||||
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