Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation

Yifan Shen; Chengwei Zhou; Yujie Gu; Hai Lin; Zhiguo Shi

doi:10.1109/spawc.2017.8227643

Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation

Yifan Shen
, Chengwei Zhou
, Yujie Gu
, Hai Lin
, Zhiguo Shi

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

18 Scopus citations

Abstract

In this paper, we propose a novel Vandermonde decomposition-based direction-of-arrival (DOA) estimation algorithm by using a coprime array, where increased number of degrees-of-freedom (DOFs) can be achieved in an off-grid manner. Specifically, the equivalent statistics corresponding to an augmented virtual uniform linear array are first derived from the coprime array received signals, and the resulting coprime coarray covariance matrix is capable to increase the DOFs. While the obtained coprime coarray covariance matrix follows a positive semi-definite Hermitian Toeplitz structure, Vandermonde decomposition can be incorporated to perform unique decomposition in the virtual domain. By matching the Vandermonde decomposition result and its theoretical version, closed-form solutions are formulated for estimating the DOA and power of each source. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

Original language	English
Title of host publication	18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781509030088
DOIs	https://doi.org/10.1109/spawc.2017.8227643
State	Published - 19 Dec 2017
Externally published	Yes
Event	18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 - Sapporo, Japan Duration: 3 Jul 2017 → 6 Jul 2017

Publication series

Name	IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume	2017-July

Conference

Conference	18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Country/Territory	Japan
City	Sapporo
Period	3/07/17 → 6/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Funding

This work has been supported by Zhejiang Provincial Natural Science Foundation (No. LR16F010002), National Natural Science Foundation of China (No. U1401253), and the Fundamental Research Funds for the Central Universities (No. 2017QNA5009).

Funders	Funder number
National Natural Science Foundation of China	U1401253
Natural Science Foundation of Zhejiang Province	LR16F010002
Fundamental Research Funds for the Central Universities	2017QNA5009

Keywords

Coprime array
Degree-of-freedom
Direction-of-arrival estimation
Vandermonde decomposition
Virtual array

Access to Document

10.1109/spawc.2017.8227643

Cite this

Shen, Y., Zhou, C., Gu, Y., Lin, H., & Shi, Z. (2017). Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation. In 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 (pp. 1-5). Article 8227643 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2017-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/spawc.2017.8227643

Shen, Yifan ; Zhou, Chengwei ; Gu, Yujie et al. / Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation. 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-5 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC).

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abstract = "In this paper, we propose a novel Vandermonde decomposition-based direction-of-arrival (DOA) estimation algorithm by using a coprime array, where increased number of degrees-of-freedom (DOFs) can be achieved in an off-grid manner. Specifically, the equivalent statistics corresponding to an augmented virtual uniform linear array are first derived from the coprime array received signals, and the resulting coprime coarray covariance matrix is capable to increase the DOFs. While the obtained coprime coarray covariance matrix follows a positive semi-definite Hermitian Toeplitz structure, Vandermonde decomposition can be incorporated to perform unique decomposition in the virtual domain. By matching the Vandermonde decomposition result and its theoretical version, closed-form solutions are formulated for estimating the DOA and power of each source. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.",

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Shen, Y, Zhou, C, Gu, Y, Lin, H & Shi, Z 2017, Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation. in 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017., 8227643, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, vol. 2017-July, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017, Sapporo, Japan, 3/07/17. https://doi.org/10.1109/spawc.2017.8227643

Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation. / Shen, Yifan; Zhou, Chengwei; Gu, Yujie et al.
18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-5 8227643 (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC; Vol. 2017-July).

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

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Shen Y, Zhou C, Gu Y, Lin H, Shi Z. Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation. In 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-5. 8227643. (IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC). doi: 10.1109/spawc.2017.8227643

Vandermonde decomposition of coprime coarray covariance matrix for DOA estimation

Abstract

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Funding

Keywords

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