2024-03-28T08:54:39Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/857312022-11-17T02:08:08Zhdl_2115_20053hdl_2115_145Accuracy Improvement in DOA Estimation with Deep LearningKase, Yuya1000070301934Nishimura, Toshihiko1000010271636Ohgane, Takeo1000070125293Ogawa, YasutakaSato, TakanoriKishiyama, Yoshihisaopen accesscopyright©2022 IEICEDOA estimationdeep learningmachine learning007Direction 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.IEICE - Institute of the Electronics, Information and Communication Engineers2022-05engjournal articleVoRhttp://hdl.handle.net/2115/85731https://doi.org/10.1587/transcom.2021EBT00010916-8516AA10826261IEICE transactions on communicationsE1055588599https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/85731/1/E105.B_2021EBT0001.pdfapplication/pdf7.96 MB2022-05