The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous présentons un algorithme adaptatif peu complexe mais précis pour le suivi de la direction d'arrivée (DOA) bidimensionnelle (2D) basé sur un réseau rectangulaire uniforme (URA). Le nouvel algorithme est un nouvel hybride de processus de suivi et de formation de faisceaux en utilisant trois étapes d'algorithmes de suivi DOA unidimensionnels (1-D) -- dans une structure arborescente hiérarchique -- pour déterminer les deux composants DOA de manière itérative de manière grossière. belle manière. Entre tous les autres algorithmes de suivi DOA 1-D, un processus de formation de faisceau orthogonal complémentaire est invoqué pour diviser les signaux entrants en groupes appropriés afin d'améliorer la précision du suivi. Étant donné que le nouvel algorithme implique uniquement l'algorithme de suivi DOA 1-D basé sur le sous-espace, la complexité globale est nettement inférieure à celle de l'extension directe bidimensionnelle (2-D) des algorithmes de suivi DOA 1-D existants, qui nécessite une mise à jour de vecteurs de dimension supérieure suivis d'une décomposition propre de dimension supérieure ou d'une recherche 2D. De plus, avec le schéma de suivi DOA structuré en arborescence, les composants DOA 2D suivis sont automatiquement appariés sans surcharge de calcul supplémentaire. Les simulations fournies montrent que le nouvel algorithme peut fournir des performances de suivi satisfaisantes dans divers scénarios.
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Kuo-Hsiung WU, Wen-Hsien FANG, "A Low Complexity Adaptive Algorithm for Eigenspace-Based Two-Dimensional Direction of Arrival Tracking" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 8, pp. 2097-2106, August 2009, doi: 10.1587/transfun.E92.A.2097.
Abstract: In this paper, we present a low complexity, yet accurate adaptive algorithm for the tracking of two-dimensional (2-D) direction of arrival (DOAs) based on a uniform rectangular array (URA). The new algorithm is a novel hybrid of tracking and beamforming processes by making use of three stages of one-dimensional (1-D) DOA tracking algorithms -- in a hierarchical tree structure -- to determine the two DOA components iteratively in a coarse-fine manner. In between every other 1-D DOA tracking algorithm, a complementary orthogonal beamforming process is invoked to partition the incoming signals into appropriate groups to enhance the tracking accuracy. Since the new algorithm only involves the 1-D subspace-based DOA tracking algorithm, the overall complexity is substantially less than the direct two-dimensional (2-D) extension of the existing 1-D DOA tracking algorithms, which requires an update of higher-dimensional vectors followed by a higher-dimensional eigendecomposition or a 2-D search. Furthermore, with the tree-structured DOA tracking scheme, the tracked 2-D DOA components are automatically paired without extra computational overhead. Furnished simulations show that the new algorithm can provide satisfactory tracking performance in various scenarios.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2097/_p
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@ARTICLE{e92-a_8_2097,
author={Kuo-Hsiung WU, Wen-Hsien FANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Low Complexity Adaptive Algorithm for Eigenspace-Based Two-Dimensional Direction of Arrival Tracking},
year={2009},
volume={E92-A},
number={8},
pages={2097-2106},
abstract={In this paper, we present a low complexity, yet accurate adaptive algorithm for the tracking of two-dimensional (2-D) direction of arrival (DOAs) based on a uniform rectangular array (URA). The new algorithm is a novel hybrid of tracking and beamforming processes by making use of three stages of one-dimensional (1-D) DOA tracking algorithms -- in a hierarchical tree structure -- to determine the two DOA components iteratively in a coarse-fine manner. In between every other 1-D DOA tracking algorithm, a complementary orthogonal beamforming process is invoked to partition the incoming signals into appropriate groups to enhance the tracking accuracy. Since the new algorithm only involves the 1-D subspace-based DOA tracking algorithm, the overall complexity is substantially less than the direct two-dimensional (2-D) extension of the existing 1-D DOA tracking algorithms, which requires an update of higher-dimensional vectors followed by a higher-dimensional eigendecomposition or a 2-D search. Furthermore, with the tree-structured DOA tracking scheme, the tracked 2-D DOA components are automatically paired without extra computational overhead. Furnished simulations show that the new algorithm can provide satisfactory tracking performance in various scenarios.},
keywords={},
doi={10.1587/transfun.E92.A.2097},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - A Low Complexity Adaptive Algorithm for Eigenspace-Based Two-Dimensional Direction of Arrival Tracking
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2097
EP - 2106
AU - Kuo-Hsiung WU
AU - Wen-Hsien FANG
PY - 2009
DO - 10.1587/transfun.E92.A.2097
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2009
AB - In this paper, we present a low complexity, yet accurate adaptive algorithm for the tracking of two-dimensional (2-D) direction of arrival (DOAs) based on a uniform rectangular array (URA). The new algorithm is a novel hybrid of tracking and beamforming processes by making use of three stages of one-dimensional (1-D) DOA tracking algorithms -- in a hierarchical tree structure -- to determine the two DOA components iteratively in a coarse-fine manner. In between every other 1-D DOA tracking algorithm, a complementary orthogonal beamforming process is invoked to partition the incoming signals into appropriate groups to enhance the tracking accuracy. Since the new algorithm only involves the 1-D subspace-based DOA tracking algorithm, the overall complexity is substantially less than the direct two-dimensional (2-D) extension of the existing 1-D DOA tracking algorithms, which requires an update of higher-dimensional vectors followed by a higher-dimensional eigendecomposition or a 2-D search. Furthermore, with the tree-structured DOA tracking scheme, the tracked 2-D DOA components are automatically paired without extra computational overhead. Furnished simulations show that the new algorithm can provide satisfactory tracking performance in various scenarios.
ER -