Performance Analysis of DOA Estimation of Two Targets Using Deep Learning

被引：7

作者：

Kase, Yuya ^{[1
]}

Nishimura, Toshihiko ^{[1
]}

Ohgane, Takeo ^{[1
]}

Ogawa, Yasutaka ^{[1
]}

Kitayama, Daisuke ^{[2
]}

Kishiyama, Yoshihisa ^{[2
]}

机构：

[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Kita Ku, Kita 14,Nishi 9, Sapporo, Hokkaido 0600814, Japan

[2] NTT DOCOMO INC, Res Labs, Hikarinooka 3-6, Yokosuka, Kanagawa 2398536, Japan

来源：

2019 22ND INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC) | 2019年

关键词：

DOA estimation; deep learning; machine learning;

D O I：

10.1109/wpmc48795.2019.9096165

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Direction of arrival (DOA) estimation of wireless signals is demanded in many situations. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing has been very common recently. Deep learning or machine learning is also known as a non-linear algorithm and now applied to various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. Thus, the accuracy may be degraded when the DOA is on the boundary. In this paper, the performance of DOA estimation using deep learning is compared with one of MUSIC which is off-grid estimation. The simulation results show that deep learning based estimation performs less well than MUSIC due to the grid boundary problem. When the allowable estimation error is relaxed, however, it is found that the success rate of DOA estimation surpass one of MUSIC.

引用

页数：6

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