This article provides an overview of a few applications of artificial intelligence (AI) in
adaptive and reconfigurable antenna arrays. In particular, AI proves to be more robust than …

Millimeter-wave sensing using automotive radar imposes high requirements on the applied
signal processing in order to obtain the necessary resolution for current imaging radar. High …

Frequency modulated continuous wave (FMCW) based millimeter wave (MMW) radar
systems have found widespread applications in advanced driving assistant system recently …

This work addresses the problem of direction-of-arrival (DOA) estimation in the presence of
non-Gaussian, heavy-tailed, and spatially-colored interference. Conventionally, the …

High-resolution automotive radar sensors are required to meet the high bar of autonomous
vehicle needs and regulations. However, current radar systems are limited in their angular …

A method for multi-target direction-of-arrival estimation for commercial FMCW radar systems
in the automotive domain is presented. The proposed approach realizes single-snapshot …

This paper introduces a novel deep learning based concept for image enhancement and
distortion suppression in automotive radar signal processing. The deep neural network …

In this paper, we discuss the usage of Residual Neural Networks (ResNets) for calculating
the Direction Of Arrival (DOA) of MIMO radars and the estimation of the number of targets …

Automotive radar plays an important role in the field of advanced driver assistant systems
(ADASs), in the detection of unmanned aerial vehicles (UAV), and so on. Most automotive …

In this paper, we propose a novel framework called DeepMLE, which gives a solution to the
single-snapshot Direction Of Arrival (DOA) estimation problem, up to 4 distinct targets, using …