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An SBL-Based Coherent Source Localization Method Using Virtual Array Output
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Summary :
Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.11 pp.2151-2158
- Publication Date
- 2019/11/01
- Publicized
- 2019/05/16
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018EBP3309
- Type of Manuscript
- PAPER
- Category
- Antennas and Propagation
Authors
Zeyun ZHANG
Nanjing University of Posts and Telecommunications
Xiaohuan WU
Nanjing University of Posts and Telecommunications
Chunguo LI
Southeast University
Wei-Ping ZHU
Concordia University,Nanjing University of Posts and Telecommunications
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Zeyun ZHANG, Xiaohuan WU, Chunguo LI, Wei-Ping ZHU, "An SBL-Based Coherent Source Localization Method Using Virtual Array Output" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 11, pp. 2151-2158, November 2019, doi: 10.1587/transcom.2018EBP3309.
Abstract: Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3309/_p
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@ARTICLE{e102-b_11_2151,
author={Zeyun ZHANG, Xiaohuan WU, Chunguo LI, Wei-Ping ZHU, },
journal={IEICE TRANSACTIONS on Communications},
title={An SBL-Based Coherent Source Localization Method Using Virtual Array Output},
year={2019},
volume={E102-B},
number={11},
pages={2151-2158},
abstract={Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.},
keywords={},
doi={10.1587/transcom.2018EBP3309},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - An SBL-Based Coherent Source Localization Method Using Virtual Array Output
T2 - IEICE TRANSACTIONS on Communications
SP - 2151
EP - 2158
AU - Zeyun ZHANG
AU - Xiaohuan WU
AU - Chunguo LI
AU - Wei-Ping ZHU
PY - 2019
DO - 10.1587/transcom.2018EBP3309
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2019
AB - Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.
ER -
English
