TY - GEN
T1 - Introduction of Direction of Arrival (DOA) Indoor and Outdoor Navigation Based on Machine Learning
AU - Liu, Tong
AU - Lin, Wei
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2026.
PY - 2025/10
Y1 - 2025/10
N2 - Direction of arrival (DOA) estimation is a significant technology in navigation and positioning systems. With the development of machine learning, DOA estimation methods based on machine learning have shown great potential in indoor and outdoor navigation. This paper reviews the theoretical background and models of DOA and gives an overview of machine learning methods applied to DOA estimation. It also discusses DOA estimation methods based on machine learning and their applications in navigation. Machine learning can be divided into traditional machine learning methods, deep learning methods, and reinforcement learning methods. Traditional machine learning methods are support vector machines, k-nearest neighbor classification, etc. Deep learning methods consist of deep neural networks, convolutional neural networks, etc. By analyzing the challenges faced by current applications and future development directions, potential strategies for enhancing the performance of DOA estimation are proposed. This study aims to provide a comprehensive technical framework and a reference for future research for researchers in related fields.
AB - Direction of arrival (DOA) estimation is a significant technology in navigation and positioning systems. With the development of machine learning, DOA estimation methods based on machine learning have shown great potential in indoor and outdoor navigation. This paper reviews the theoretical background and models of DOA and gives an overview of machine learning methods applied to DOA estimation. It also discusses DOA estimation methods based on machine learning and their applications in navigation. Machine learning can be divided into traditional machine learning methods, deep learning methods, and reinforcement learning methods. Traditional machine learning methods are support vector machines, k-nearest neighbor classification, etc. Deep learning methods consist of deep neural networks, convolutional neural networks, etc. By analyzing the challenges faced by current applications and future development directions, potential strategies for enhancing the performance of DOA estimation are proposed. This study aims to provide a comprehensive technical framework and a reference for future research for researchers in related fields.
KW - Direction of arrival
KW - machine learning
KW - navigation systems
UR - https://www.scopus.com/pages/publications/105019294285
U2 - 10.1007/978-3-031-96146-5_15
DO - 10.1007/978-3-031-96146-5_15
M3 - Conference article published in proceeding or book
AN - SCOPUS:105019294285
SN - 9783031961458
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 200
EP - 214
BT - Smart Grid and Innovative Frontiers in Telecommunications - 9th EAI International Conference, SmartGift 2024, Proceedings
A2 - Lau, Francis C. M.
A2 - Ho, Ivan W. H.
A2 - Lai, Edmund
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2024
Y2 - 9 December 2024 through 10 December 2024
ER -