Source Estimation Using Coprime Array: A Sparse Reconstruction Perspective

Direction-of-arrival (DOA), power, and achievable degrees-of-freedom (DOFs) are fundamental parameters for source estimation. In this paper, we propose a novel sparse reconstruction-based source estimation algorithm by using a coprime array. Specifically, a difference coarray is derived from a copri...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 17; no. 3; pp. 755 - 765
Main Authors: Zhiguo Shi, Chengwei Zhou, Yujie Gu, Goodman, Nathan A., Fengzhong Qu
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computer simulation
Coprime array
Covariance matrices
Covariance matrix
Direction of arrival
Direction-of-arrival estimation
DOA estimation
Estimation
Image reconstruction
Linear matrix inequalities
Mathematical analysis
Matrix methods
Parameter estimation
power estimation
Reconstruction
Sensor arrays
source enumeration
sparse reconstruction
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Direction-of-arrival (DOA), power, and achievable degrees-of-freedom (DOFs) are fundamental parameters for source estimation. In this paper, we propose a novel sparse reconstruction-based source estimation algorithm by using a coprime array. Specifically, a difference coarray is derived from a coprime array as the foundation for increasing the number of DOFs, and a virtual uniform linear subarray covariance matrix sparse reconstruction-based optimization problem is formulated for DOA estimation. Meanwhile, a modified sliding window scheme is devised to remove the spurious peaks from the reconstructed sparse spatial spectrum, and the power estimation is enhanced through a least squares problem. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of DOA estimation and power estimation as well as the achievable DOFs.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2016.2637059