Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction

Chengwei Zhou; Zhiguo Shi; Yujie Gu; Yimin D. Zhang

doi:10.1109/icassp.2018.8461721

Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction

Chengwei Zhou, Zhiguo Shi, Yujie Gu, Yimin D. Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

Abstract

Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3479-3483
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/icassp.2018.8461721
State	Published - 10 Sep 2018
Externally published	Yes
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (Print)	1520-6149

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Coarray interpolation
Coprime array
Covariance matrix reconstruction
DOA estimation
Rotational invariance

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10.1109/icassp.2018.8461721

Cite this

Zhou, C., Shi, Z., Gu, Y., & Zhang, Y. D. (2018). Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 3479-3483). Article 8461721 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icassp.2018.8461721

Zhou, Chengwei ; Shi, Zhiguo ; Gu, Yujie et al. / Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3479-3483 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction",

abstract = "Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.",

keywords = "Coarray interpolation, Coprime array, Covariance matrix reconstruction, DOA estimation, Rotational invariance",

author = "Chengwei Zhou and Zhiguo Shi and Yujie Gu and Zhang, {Yimin D.}",

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doi = "10.1109/icassp.2018.8461721",

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Zhou, C, Shi, Z, Gu, Y & Zhang, YD 2018, Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461721, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 3479-3483, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/icassp.2018.8461721

Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction. / Zhou, Chengwei; Shi, Zhiguo; Gu, Yujie et al.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3479-3483 8461721 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhou, Chengwei

AU - Shi, Zhiguo

AU - Gu, Yujie

AU - Zhang, Yimin D.

PY - 2018/9/10

Y1 - 2018/9/10

N2 - Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

AB - Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

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ER -

Zhou C, Shi Z, Gu Y, Zhang YD. Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3479-3483. 8461721. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/icassp.2018.8461721

Coarray Interpolation-Based Coprime Array Doa Estimation Via Covariance Matrix Reconstruction

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