In this work, we consider direction-of-arrival (DoA) estimation in the presence of extreme
noise using Deep Learning (DL). In particular, we introduce a Convolutional Neural Network …

This paper examines the relationship between conventional beamforming and linear
supervised learning, then develops a nonlinear deep feed-forward neural network (FNN) for …

In this paper, we study the problem of direction of arrival estimation and model order
selection for systems employing subarray sampling. Thereby, we focus on scenarios, where …

Recently, synchrosqueenzing transform (SST)-based time-frequency analysis (TFA)
methods have been developed for achieving the highly concentrated TF representation …

Frequency estimation is a fundamental problem in signal processing, with applications in
radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to …

In this article, we investigate the feasibility of a high angular resolution method based on
deep learning for estimating the angle of arrival (AoA) using an automotive frequency …

Frequency estimation of 2-D multicomponent sinusoidal signals is a fundamental issue in
the statistical signal processing community that arises in various disciplines. In this article …

Remote passive sonar detection with low-frequency band spectral lines has attracted much
attention, while complex low-frequency non-Gaussian impulsive noisy environments would …

Ultrasonic fatigue tests (UFT) are used to study the fatigue life behavior of metallic
components undergoing a very high number of cycles (typically≥ 1 0 7− 1 0 9 cycles) under …

Frequency estimation of multiple sinusoidals is fundamental in statistical signal processing.
In this article, we focus on 1-D complex signals and propose a complex-valued network for …