DOA Estimation of Coherently Distributed Sources Based on Block-Sparse Constraint
Summary :
In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.7 pp.2472-2476
- Publication Date
- 2012/07/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.2472
- Type of Manuscript
- LETTER
- Category
- Antennas and Propagation
Authors
Keyword
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Lu GAN, Xiao Qing WANG, Hong Shu LIAO, "DOA Estimation of Coherently Distributed Sources Based on Block-Sparse Constraint" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 7, pp. 2472-2476, July 2012, doi: 10.1587/transcom.E95.B.2472.
Abstract: In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.2472/_p
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@ARTICLE{e95-b_7_2472,
author={Lu GAN, Xiao Qing WANG, Hong Shu LIAO, },
journal={IEICE TRANSACTIONS on Communications},
title={DOA Estimation of Coherently Distributed Sources Based on Block-Sparse Constraint},
year={2012},
volume={E95-B},
number={7},
pages={2472-2476},
abstract={In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.},
keywords={},
doi={10.1587/transcom.E95.B.2472},
ISSN={1745-1345},
month={July},}
Copy
TY - JOUR
TI - DOA Estimation of Coherently Distributed Sources Based on Block-Sparse Constraint
T2 - IEICE TRANSACTIONS on Communications
SP - 2472
EP - 2476
AU - Lu GAN
AU - Xiao Qing WANG
AU - Hong Shu LIAO
PY - 2012
DO - 10.1587/transcom.E95.B.2472
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2012
AB - In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.
ER -
English
