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Sensors 2018, 18(1), 253; https://doi.org/10.3390/s18010253

Underdetermined Wideband DOA Estimation for Off-Grid Sources with Coprime Array Using Sparse Bayesian Learning

1
Institute of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Received: 29 November 2017 / Revised: 8 January 2018 / Accepted: 11 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Abstract

Sparse Bayesian learning (SBL) is applied to the coprime array for underdetermined wideband direction of arrival (DOA) estimation. Using the augmented covariance matrix, the coprime array can achieve a higher number of degrees of freedom (DOFs) to resolve more sources than the number of physical sensors. The sparse-based DOA estimation can deteriorate the detection and estimation performance because the sources may be off the search grid no matter how fine the grid is. This dictionary mismatch problem can be well resolved by the SBL using fixed point updates. The SBL can automatically choose sparsity and approximately resolve the non-convex optimizaton problem. Numerical simulations are conducted to validate the effectiveness of the underdetermined wideband DOA estimation via SBL based on coprime array. It is clear that SBL can obtain good performance in detection and estimation compared to least absolute shrinkage and selection operator (LASSO), simultaneous orthogonal matching pursuit least squares (SOMP-LS) , simultaneous orthogonal matching pursuit total least squares (SOMP-TLS) and off-grid sparse Bayesian inference (OGSBI). View Full-Text
Keywords: coprime array; direction of arrival estimation; degrees of freedom; Sparse Bayesian learning; sparse signal representation; off-grid sources coprime array; direction of arrival estimation; degrees of freedom; Sparse Bayesian learning; sparse signal representation; off-grid sources
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Qin, Y.; Liu, Y.; Liu, J.; Yu, Z. Underdetermined Wideband DOA Estimation for Off-Grid Sources with Coprime Array Using Sparse Bayesian Learning. Sensors 2018, 18, 253.

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