This article aims to constitute a noteworthy contribution to the domain of direction-of-arrival
(DoA) estimation through the application of deep learning algorithms. We approach the DoA …

To achieve fast and accurate two-dimensional (2D) direction of arrival (DOA) estimation, a
novel deep ensemble learning method is presented in this paper. First, a convolutional …

This paper presents an experimental validation of deep learning-based direction-of-arrival
(DoA) estimation by using realistic data collected via universal software radio peripheral …

Lacking of adaptation to various array imperfections is an open problem for most high-
precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods …

An efficient neural network-based approach for broadband direction of arrival (DOA)
estimation is presented in this paper. The received data of the uniform circle array (UCA) is …

This article aims to present a novel direction of arrival (DOA) estimation strategy in multipath
environments using deep learning. Eigen decomposition‐based algorithms, such as …

This work utilizes convolutional neural networks (CNNs) to estimate the directions of arrival
of plane waves impinging on an array of sensors. We propose a methodology to impose the …

We develop a deep learning framework for Direction of Arrival (DOA) estimation. The sparse
power spectrum inspires us, and the first shows that the columns of the array covariance …

Received signal Direction of Arrival (DOA) estimation represents a significant problem with
multiple applications, ranging from wireless communications to radars. This problem …

Direction-of-arrival (DOA) estimation is a vital research topic in array signal processing, with
extensive applications in many fields. In recent years, deep learning has been applied to …