Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction

Yu Zhang; Yue Wang; Zhi Tian; G. Leus; Gong Zhang

doi:10.23919/Eusipco47968.2020.9287680

Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction

Yu Zhang, Yue Wang, Zhi Tian, G. Leus, Gong Zhang

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

25 Downloads (Pure)

Abstract

This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results

Original language	English
Title of host publication	28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
Place of Publication	Amsterdam (Netherlands)
Publisher	Eurasip
Pages	1876-1880
Number of pages	5
ISBN (Electronic)	978-9-0827-9705-3
DOIs	https://doi.org/10.23919/Eusipco47968.2020.9287680
Publication status	Published - 2020
Event	EUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands Duration: 18 Jan 2021 → 22 Jan 2021 Conference number: 28th

Publication series

Name	European Signal Processing Conference
Volume	2021-January
ISSN (Print)	2219-5491

Conference

Conference	EUSIPCO 2020
Country/Territory	Netherlands
City	Amsterdam
Period	18/01/21 → 22/01/21
Other	Date change due to COVID-19 (former date August 24-28 2020)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

2D harmonic retrieval
Covariance reconstruction
D-ANM
Low complexity
RR transformation

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Low-Complexity_Gridless_2D_Harmonic_Retrieval_via_Decoupled-ANM_Covariance_ReconstructionFinal published version, 299 KB

Cite this

Zhang, Y., Wang, Y., Tian, Z., Leus, G., & Zhang, G. (2020). Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction. In 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings (pp. 1876-1880). Article 9287680 (European Signal Processing Conference; Vol. 2021-January). Eurasip. https://doi.org/10.23919/Eusipco47968.2020.9287680

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title = "Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction",

abstract = "This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results ",

keywords = "2D harmonic retrieval, Covariance reconstruction, D-ANM, Low complexity, RR transformation",

author = "Yu Zhang and Yue Wang and Zhi Tian and G. Leus and Gong Zhang",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; EUSIPCO 2020 : The 28th European Signal Processing Conference ; Conference date: 18-01-2021 Through 22-01-2021",

year = "2020",

doi = "10.23919/Eusipco47968.2020.9287680",

language = "English",

series = "European Signal Processing Conference",

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pages = "1876--1880",

booktitle = "28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings",

}

Zhang, Y, Wang, Y, Tian, Z, Leus, G & Zhang, G 2020, Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction. in 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings., 9287680, European Signal Processing Conference, vol. 2021-January, Eurasip, Amsterdam (Netherlands), pp. 1876-1880, EUSIPCO 2020, Amsterdam, Netherlands, 18/01/21. https://doi.org/10.23919/Eusipco47968.2020.9287680

Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction. / Zhang, Yu; Wang, Yue; Tian, Zhi et al.
28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings. Amsterdam (Netherlands): Eurasip, 2020. p. 1876-1880 9287680 (European Signal Processing Conference; Vol. 2021-January).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction

AU - Zhang, Yu

AU - Wang, Yue

AU - Tian, Zhi

AU - Leus, G.

AU - Zhang, Gong

N1 - Conference code: 28th

PY - 2020

Y1 - 2020

N2 - This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results

AB - This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results

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M3 - Conference contribution

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BT - 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings

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ER -

Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction

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Keywords

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