Low complexity DOA estimation using AMP with unitary transformation and iterative refinement

•A fast off-grid DOA estimator is proposed using UTAMP and iterative refinement.•The Jacobi or Gauss-Seidel iteration is used to achieve efficient refinement.•The proposed estimator has lower complexity but delivers better performance. This work deals with the problem of fast direction-of-arrival (D...

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Veröffentlicht in:Digital signal processing Jg. 106; S. 102800
Hauptverfasser: Mao, Yiwen, Luo, Man, Gao, Dawei, Guo, Qinghua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.11.2020
Schlagworte:
Approximate message passing
Compressive sensing
DOA estimation
Gauss-Seidel method
Jacobi method
DOA estimation
Compressive sensing
Approximate message passing
Gauss-Seidel method
Jacobi method
ISSN:1051-2004, 1095-4333
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Zusammenfassung:•A fast off-grid DOA estimator is proposed using UTAMP and iterative refinement.•The Jacobi or Gauss-Seidel iteration is used to achieve efficient refinement.•The proposed estimator has lower complexity but delivers better performance. This work deals with the problem of fast direction-of-arrival (DOA) estimation. A low complexity iterative off-grid method is proposed, which employs the approximate message passing with unitary transformation based sparse Bayesian learning (SBL) to obtain initial estimates of the signals and their corresponding DOAs, and then refines the estimates iteratively using the Jacobi or Gauss-Seidel iteration with low complexity. Both general array and uniform linear array (ULA) are considered. Simulation results demonstrate that, with much lower complexity, the proposed method outperforms state-of-the-art methods, and its performance can approach the Cramer-Rao bound closely.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2020.102800