Automotive radar is mandated to provide high accuracy direction of arrival (DOA) estimation
for safe driving, while remaining a low cost device for feasible mass production. Sparse …

Strategic subsampling has become a focal point due to its effectiveness in compressing
data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This …

The paper examines neural network learning of the sparse array configurations in optimum
beamforming. Unlike iterative greedy, convex, and global optimization methods for optimum …

Finite-rate-of-innovation (FRI) signal model is well suited for time-of-flight imaging
applications such as ultrasound, lidar, sonar, radar, and more. Due to their finite degrees of …

Finite-rate-of-innovation (FRI) signals are ubiquitous in radar, ultrasound, and time of flight
imaging applications. In this paper, we propose a model-based deep learning approach to …

Estimation of User Terminals'(UTs') Angle of Arrival (AoA) plays a significant role in the next
generation of wireless systems. Due to high demands, energy efficiency concerns, and …

Compressive sensing (CS) techniques for estimating the direction-of-arrival (DoA) stand
apart from traditional approaches due to their ability to derive DoA information from just a …

In the problem of sensor selection, observations from L out of N sensors are chosen for data
estimation, with the objective of minimizing an error metric. It is well known that a greedy …

Graph signals are functions of the underlying graph. When the edge-weight between a pair
of nodes is high, the corresponding signals generally have a higher correlation. As a result …

This chapter considers the joint sparse optimization of multiple-input multiple-output (MIMO)
radar transmitter and receiver arrays for improved beamforming while reducing the system …