Raza, A., Liu, W. orcid.org/0000-0003-2968-2888 and Shen, Q. (2017) Thinned coprime arrays for DOA estimation. In: 25th European Signal Processing Conference (EUSIPCO). 25th European Signal Processing Conference (EUSIPCO), 28 Aug - 02 Sep 2017, Kos, Greece. IEEE , pp. 395-399. ISBN 978-0-9928626-7-1
Abstract
Sparse arrays can generate a larger aperture than traditional uniform linear arrays (ULA) and offer enhanced degrees-of-freedom (DOFs) which can be exploited in both beamforming and direction-of-arrival (DOA) estimation. One class of sparse arrays is the coprime array, composed of two uniform linear subarrays which yield an effective difference co-array with higher number of DOFs. In this work, we present a new coprime array structure termed thinned coprime array (TCA), which exploits the redundancy in the structure of the existing coprime array and achieves the same virtual aperture and DOFs as the conventional coprime array with much fewer number of sensors. An analysis of the DOFs provided by the new structure in comparison with other sparse arrays is provided and simulation results for DOA estimation using the compressive sensing based method are provided.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 EURASIP / IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jun 2017 09:20 |
Last Modified: | 23 Feb 2018 09:22 |
Published Version: | https://doi.org/10.23919/EUSIPCO.2017.8081236 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/EUSIPCO.2017.8081236 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117154 |