2006Unpublished venueRequires access

Weighted signal-subspace direction-finding of ultra-wideband sources

Hengameh Keshavarz

Open publisher page 21 citations

Abstract

Weighted signal-subspace direction-finding is herein proposed for ultra-wideband (UWB) sources. That is, different frequencies' signal subspaces are respectively focused, weighted using the UWB signal's power spectral density, and then combined into a coherent signal subspace. Furthermore, the optimal focusing matrices are derived to minimize both the focusing error and the direction-of-arrival (DOA) estimation bias. Simulations verify the algorithm's efficiency for multipath channels.

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What this paper is about

Weighted signal-subspace direction-finding is herein proposed for ultra-wideband (UWB) sources. That is, different frequencies' signal subspaces are respectively focused, weighted using the UWB signal's power spectral density, and then combined into a coherent signal subspace. Furthermore, the optimal focusing matrices are derived to minimize both the focusing error and the direction-of-arrival (DOA) estimation bias. Simulations verify the algorithm's efficiency for multipath channels.

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Available abstract

Weighted signal-subspace direction-finding is herein proposed for ultra-wideband (UWB) sources. That is, different frequencies' signal subspaces are respectively focused, weighted using the UWB signal's power spectral density, and then combined into a coherent signal subspace. Furthermore, the optimal focusing matrices are derived to minimize both the focusing error and the direction-of-arrival (DOA) estimation bias. Simulations verify the algorithm's efficiency for multipath channels.

Key concepts: Signal subspace, Subspace topology, Multipath propagation, SIGNAL (programming language), Direction of arrival, Linear subspace, Computer science, Ultra-wideband

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