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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Accuracy Improvement in DOA Estimation with Deep Learning
| Title: | Accuracy Improvement in DOA Estimation with Deep Learning |
| Authors: | Kase, Yuya Browse this author | | Nishimura, Toshihiko Browse this author →KAKEN DB | | Ohgane, Takeo Browse this author →KAKEN DB | | Ogawa, Yasutaka Browse this author →KAKEN DB | | Sato, Takanori Browse this author | | Kishiyama, Yoshihisa Browse this author |
| Keywords: | DOA estimation | | deep learning | | machine learning |
| Issue Date: | May-2022 |
| Publisher: | IEICE - Institute of the Electronics, Information and Communication Engineers |
| Journal Title: | IEICE transactions on communications |
| Volume: | E105B |
| Issue: | 5 |
| Start Page: | 588 |
| End Page: | 599 |
| Publisher 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. |
| Rights: | copyright©2022 IEICE |
| Type: | article |
| URI: | http://hdl.handle.net/2115/85731 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 大鐘 武雄
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