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 …

In this paper, we consider the problem of direction of arrival (DOA) estimation in the
presence of sensor gain-phase errors. Under some mild assumptions, we propose a new …

An algorithm is presented for direction-of-arrival (DOA) estimation in the presence of
unknown mutual coupling based on the generalized eigenvalues utilizing signal subspace …

In this article, we summarized the topics of array processing for wideband signals in smart
antenna-based applications. For wideband beamforming, the TDF1B and FDFIB methods …

Array imperfections severely degrade the performance of most physics-driven direction-of-
arrival (DOA) methods. Deep learning-based methods do not rely on any assumptions, can …

Array calibration with angularly dependent gain and phase uncertainties has long been a
difficult problem. Although many array calibration methods have been reported extensively …

In practice, unavoidable array errors, consisting of phase and position errors, significantly
degrade the performance of constellation synthetic aperture radar (SAR) systems. Therefore …

This paper introduces an innovative unified deep learning (DL) model,“reduced deep
convolutional stack autoencoder (RDCSAE)-robust kernel-based random vector functional …

Low overhead channel estimation based on compressive sensing (CS) has been widely
investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output …

Array shape calibration is commonly categorized into active calibration and self-calibration,
yet both methods have their drawbacks and are generally considered less optimal. This …