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Convolutional Beamspace for Array Signal Processing

Vaidyanathan, P. P. and Chen, Po-Chih (2020) Convolutional Beamspace for Array Signal Processing. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE , Piscataway, NJ, pp. 4707-4711. ISBN 9781509066315. https://resolver.caltech.edu/CaltechAUTHORS:20200417-132226845

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Abstract

A new type of beamspace for array processing is introduced called convolutional beamspace. It enjoys the advantages of traditional beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike traditional beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without any overhead of preparation, as the Vandermonde structure and the shift-invariance are preserved under the transformation. The method produces more accurate DOA estimates than traditional beamspace methods, and for correlated sources it produces better estimates than element-space methods.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ICASSP40776.2020.9054051DOIArticle
ORCID:
AuthorORCID
Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2020 IEEE. This work was supported in parts by the ONR grant N00014-18-1-2390, the NSF grant CCF-1712633, and the California Institute of Technology
Funders:
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-18-1-2390
NSFCCF-1712633
CaltechUNSPECIFIED
Subject Keywords:Beamspace, Convolution, Large Arrays, Dimension Reduction, MUSIC
Record Number:CaltechAUTHORS:20200417-132226845
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200417-132226845
Official Citation:P. P. Vaidyanathan and P. Chen, "Convolutional Beamspace for Array Signal Processing," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 4707-4711; doi: 10.1109/ICASSP40776.2020.9054051
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102606
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Apr 2020 21:04
Last Modified:17 Apr 2020 21:04

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