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Accuracy Improvement in DOA Estimation with Deep Learning
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Summary :
Direction of arrival (DOA) estimation of wireless signals is demanded in many applications. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing have become common subjects of study recently. Deep learning or machine learning is also known as a non-linear algorithm and has been applied in various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. A major problem of on-grid estimation is that the accuracy may be degraded when the DOA is near the boundary. To reduce such estimation errors, we propose a method of combining two DNNs whose grids are offset by one half of the grid size. Simulation results show that our proposal outperforms MUSIC which is a typical off-grid estimation method. Furthermore, it is shown that the DNN specially trained for a close DOA case achieves very high accuracy for that case compared with MUSIC.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.5 pp.588-599
- Publication Date
- 2022/05/01
- Publicized
- 2021/12/01
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBT0001
- Type of Manuscript
- PAPER
- Category
- Antennas and Propagation
Authors
Yuya KASE
Hokkaido University
Toshihiko NISHIMURA
Hokkaido University
Takeo OHGANE
Hokkaido University
Yasutaka OGAWA
Hokkaido University
Takanori SATO
Hokkaido University
Yoshihisa KISHIYAMA
NTT DOCOMO, INC.
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Yuya KASE, Toshihiko NISHIMURA, Takeo OHGANE, Yasutaka OGAWA, Takanori SATO, Yoshihisa KISHIYAMA, "Accuracy Improvement in DOA Estimation with Deep Learning" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 5, pp. 588-599, May 2022, doi: 10.1587/transcom.2021EBT0001.
Abstract: Direction of arrival (DOA) estimation of wireless signals is demanded in many applications. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing have become common subjects of study recently. Deep learning or machine learning is also known as a non-linear algorithm and has been applied in various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. A major problem of on-grid estimation is that the accuracy may be degraded when the DOA is near the boundary. To reduce such estimation errors, we propose a method of combining two DNNs whose grids are offset by one half of the grid size. Simulation results show that our proposal outperforms MUSIC which is a typical off-grid estimation method. Furthermore, it is shown that the DNN specially trained for a close DOA case achieves very high accuracy for that case compared with MUSIC.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBT0001/_p
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@ARTICLE{e105-b_5_588,
author={Yuya KASE, Toshihiko NISHIMURA, Takeo OHGANE, Yasutaka OGAWA, Takanori SATO, Yoshihisa KISHIYAMA, },
journal={IEICE TRANSACTIONS on Communications},
title={Accuracy Improvement in DOA Estimation with Deep Learning},
year={2022},
volume={E105-B},
number={5},
pages={588-599},
abstract={Direction of arrival (DOA) estimation of wireless signals is demanded in many applications. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing have become common subjects of study recently. Deep learning or machine learning is also known as a non-linear algorithm and has been applied in various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. A major problem of on-grid estimation is that the accuracy may be degraded when the DOA is near the boundary. To reduce such estimation errors, we propose a method of combining two DNNs whose grids are offset by one half of the grid size. Simulation results show that our proposal outperforms MUSIC which is a typical off-grid estimation method. Furthermore, it is shown that the DNN specially trained for a close DOA case achieves very high accuracy for that case compared with MUSIC.},
keywords={},
doi={10.1587/transcom.2021EBT0001},
ISSN={1745-1345},
month={May},}
Copy
TY - JOUR
TI - Accuracy Improvement in DOA Estimation with Deep Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 588
EP - 599
AU - Yuya KASE
AU - Toshihiko NISHIMURA
AU - Takeo OHGANE
AU - Yasutaka OGAWA
AU - Takanori SATO
AU - Yoshihisa KISHIYAMA
PY - 2022
DO - 10.1587/transcom.2021EBT0001
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2022
AB - Direction of arrival (DOA) estimation of wireless signals is demanded in many applications. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing have become common subjects of study recently. Deep learning or machine learning is also known as a non-linear algorithm and has been applied in various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. A major problem of on-grid estimation is that the accuracy may be degraded when the DOA is near the boundary. To reduce such estimation errors, we propose a method of combining two DNNs whose grids are offset by one half of the grid size. Simulation results show that our proposal outperforms MUSIC which is a typical off-grid estimation method. Furthermore, it is shown that the DNN specially trained for a close DOA case achieves very high accuracy for that case compared with MUSIC.
ER -
English
