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Convolutional Beamspace for Linear Arrays

Chen, Po-Chih and Vaidyanathan, P. P. (2020) Convolutional Beamspace for Linear Arrays. IEEE Transactions on Signal Processing, 68 . pp. 5395-5410. ISSN 1053-587X. https://resolver.caltech.edu/CaltechAUTHORS:20201016-150358626

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Abstract

A new beamspace method for array processing, called convolutional beamspace (CBS), is proposed. It enjoys the advantages of classical beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike classical beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure and the shift-invariance are preserved under the CBS transformation. The method produces more accurate DOA estimates than classical beamspace, and for correlated sources, better estimates than element-space. The method also generalizes to sparse arrays by effective use of the difference coarray. For this, the autocorrelation evaluated on the ULA portion of the coarray is filtered appropriately to produce the coarray CBS. It is also shown how CBS can be used in the context of sparse signal representation with dictionaries, where the dictionaries have columns that resemble steering vectors at a dense grid of frequencies. Again CBS processing with dictionaries offers better resolution, accuracy, and lower computational complexity. As only the filter responses at discrete frequencies on the dictionary grid are relevant, the problem of designing discrete-frequency FIR filters is also addressed.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tsp.2020.3021670DOIArticle
ORCID:
AuthorORCID
Chen, Po-Chih0000-0003-1637-9329
Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2020 IEEE. Manuscript received February 21, 2020; revised June 19, 2020 and August 21, 2020; accepted August 31, 2020. Date of publication September 4, 2020; date of current version September 29, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. David Ramirez. This work was supported in parts by the ONR under Grant N00014-18-1-2390, the NSF under 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:Convolutional beamspace, DOA estimation, sparse arrays, dictionaries, root-MUSIC, ESPRIT
Record Number:CaltechAUTHORS:20201016-150358626
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201016-150358626
Official Citation:P. -C. Chen and P. P. Vaidyanathan, "Convolutional Beamspace for Linear Arrays," in IEEE Transactions on Signal Processing, vol. 68, pp. 5395-5410, 2020, doi: 10.1109/TSP.2020.3021670
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106123
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:16 Oct 2020 22:19
Last Modified:16 Oct 2020 22:19

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