Performance Analysis of DOA Estimation of Two Targets Using Deep Learning

被引：5

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

共 50 条

[41] Determining the meter of classical Arabic poetry using deep learning: a performance analysis [J].

Mutawa, A. M. ;

Alrumaih, Ayshah .

FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2025, 8

[42] Using Deep Learning for Sonar Targets Localization [J].

Bruel, Q. ;

Heitzmann, F. ;

Morche, D. ;

Huillery, J. ;

Blanco, E. ;

Bako, L. .

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN SIGNAL PROCESSING AND ARTIFICIAL INTELLIGENCE, ASPAI' 2020, 2020, :77-81

[43] FAST DIRECTION-OF-ARRIVAL ESTIMATION OF MULTIPLE TARGETS USING DEEP LEARNING AND SPARSE ARRAYS [J].

Papageorgiou, Georgios K. ;

Sellathurai, Mathini .

2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, :4632-4636

[44] Deep Learning Based DOA Estimation With Trainable-Step-Size LMS Algorithm [J].

Guo, Yu ;

Zhang, Zhi ;

Huang, Yuzhen .

2023 IEEE 34TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, PIMRC, 2023,

[45] Age estimation using deep learning [J].

Zaghbani, Soumaya ;

Boujneh, Noureddine ;

Bouhlel, Med Salim .

COMPUTERS & ELECTRICAL ENGINEERING, 2018, 68 :337-347

[46] Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving [J].

Wan, Liangtian ;

Sun, Yuchen ;

Sun, Lu ;

Ning, Zhaolong ;

Rodrigues, Joel J. P. C. .

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (07) :4301-4315

[47] Sentiment Analysis using Machine Learning and Deep Learning [J].

Chandra, Yogesh ;

Jana, Antoreep .

PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM-2020), 2019, :1-4

[48] Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning [J].

Chang Woo Hong ;

Jeongju Kim .

Journal of Electrical Engineering & Technology, 2023, 18 :3117-3125

[49] KPI based performance estimation in production systems using deep learning techniques [J].

Akkur, Taha ;

Saricicek, Inci .

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2024, 39 (03) :1499-1507

[50] Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning [J].

Hong, Chang Woo ;

Kim, Jeongju .

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2023, 18 (04) :3117-3125

← 1 2 3 4 5 →