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Convolutional Beamspace and Sparse Signal Recovery for Linear Arrays

Chen, Po-Chih and Vaidyanathan, P. P. (2020) Convolutional Beamspace and Sparse Signal Recovery for Linear Arrays. In: 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE , Piscataway, NJ, pp. 929-933. ISBN 978-0-7381-3126-9.

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The convolutional beamspace (CBS) method for DOA estimation using dictionary-based sparse signal recovery is introduced. Beamspace methods enjoy lower computational complexity, increased parallelism of subband processing, and improved DOA resolution. But unlike classical beamspace methods, CBS allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure for ULAs are preserved in the CBS output. Due to the same reason, it is shown in this paper that sparse signal representation problems can also be directly formulated on the CBS output. Significant reduction in computational complexity and higher probability of resolution are obtained by using CBS. It is also shown how the regularization parameter involved in the method should be chosen.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Chen, Po-Chih0000-0003-1637-9329
Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2020 IEEE. This work was supported in parts by the NSF grant CCF-1712633, the ONR grant N00014-18-1-2390, and the California Institute of Technology.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-18-1-2390
Subject Keywords:Convolutional beamspace, DOA estimation, linear sensor arrays, sparse signal recovery, dictionaries
Record Number:CaltechAUTHORS:20210622-215812982
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Official Citation:P. -C. Chen and P. P. Vaidyanathan, "Convolutional Beamspace and Sparse Signal Recovery for Linear Arrays," 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020, pp. 929-933, doi: 10.1109/IEEECONF51394.2020.9443522
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109539
Deposited By: Tony Diaz
Deposited On:23 Jun 2021 18:47
Last Modified:23 Jun 2021 18:47

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