This preprint is under consideration at EURASIP Journal on Advances in Signal Processing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Di Zhao
Beijing University of Posts and Telecommunications
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4527-338X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the e ectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.
Keywords
Direction-of-arrival estimation, uniform circular array, low complexity, beamspace transformation, convex optimization
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CURRENT STATUS: Under Review
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Received 04 Apr, 2021
Reviewer #4 agreed
On 18 Mar, 2021
Reviewer #3 agreed
On 12 Mar, 2021
Reviewer #2 agreed
On 12 Mar, 2021
Reviewer #1 agreed
On 11 Mar, 2021
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Received 11 Mar, 2021
Reviewers invited
Invitations sent on 09 Mar, 2021
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On 08 Mar, 2021
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First submitted
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Subject Areas
Laboratory Diagnostics
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This preprint is under consideration at EURASIP Journal on Advances in Signal Processing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Di Zhao
Beijing University of Posts and Telecommunications
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4527-338X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 15 Mar, 2021
No community comments so far
Review #3 received
Received 09 Apr, 2021
Review #2 received
Received 05 Apr, 2021
Review #1 received
Received 04 Apr, 2021
Reviewer #4 agreed
On 18 Mar, 2021
Reviewer #3 agreed
On 12 Mar, 2021
Reviewer #2 agreed
On 12 Mar, 2021
Reviewer #1 agreed
On 11 Mar, 2021
Review #? received
Received 11 Mar, 2021
Reviewers invited
Invitations sent on 09 Mar, 2021
Editor assigned
On 08 Mar, 2021
Submission checks complete
On 08 Mar, 2021
Editor invited
On 08 Mar, 2021
First submitted
On 25 Feb, 2021
Subject Areas
Laboratory Diagnostics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the e ectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This preprint is available for download as a PDF.
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