Accuracy Improvement in DOA Estimation with Deep Learning
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- KASE Yuya
- Graduate School/Faculty of Information Science and Technology, Hokkaido University
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- NISHIMURA Toshihiko
- Graduate School/Faculty of Information Science and Technology, Hokkaido University
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- OHGANE Takeo
- Graduate School/Faculty of Information Science and Technology, Hokkaido University
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- OGAWA Yasutaka
- Graduate School/Faculty of Information Science and Technology, Hokkaido University
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- SATO Takanori
- Graduate School/Faculty of Information Science and Technology, Hokkaido University
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- KISHIYAMA Yoshihisa
- Research Laboratories, NTT DOCOMO, INC.
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<p>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.</p>
収録刊行物
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- IEICE Transactions on Communications
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IEICE Transactions on Communications E105.B (5), 588-599, 2022-05-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390291932646157184
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- NII論文ID
- 130008123240
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- NII書誌ID
- AA10826261
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- ISSN
- 17451345
- 09168516
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- HANDLE
- 2115/85731
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可