Direction-of-arrival estimation by subspace rotation methods - ESPRIT
R. Roy, Arogyaswami J. Paulraj, T. Kailath
Abstract
R. Roy, Arogyaswami J. Paulraj, T. Kailath
Abstract
Results of simulations comparing the performance of ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) and the MUSIC (MUltiple Signal Classification) algorithm are presented. ESPRIT exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. In contrast, the MUSIC algorithm uses intersections between the array manifold and the signal subspace to estimate the directions. ESPRIT is shown to have performance advantages over MUSIC in certain scenarios apart from its previously reported implementational advantages.
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Results of simulations comparing the performance of ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) and the MUSIC (MUltiple Signal Classification) algorithm are presented. ESPRIT exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. In contrast, the MUSIC algorithm uses intersections between the array manifold and the signal subspace to estimate the directions. ESPRIT is shown to have performance advantages over MUSIC in certain scenarios apart from its previously reported implementational advantages.
Key concepts: Rotational invariance, Signal subspace, Linear subspace, Subspace topology, SIGNAL (programming language), Direction of arrival, Manifold (fluid mechanics), Rotation (mathematics)