Efficient Embedded Element Pattern Prediction via Machine Learning: A Case Study with Planar Non-Uniform Sub-Arrays

Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in...

Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing

The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed. In this method, the angular space is discretized, defining many non-overlapping small grids which cover the desired large angular space. First, a BCS...

Total Variation Compressive Sensing for Extended Targets in MIMO Radar

The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied...

Enhancing Angular Resolution Using Neural Networks in Automotive Radars

Poor angular resolution is one of the main disadvantages of automotive radars, and the reason why lidar technology is widely used in the automotive industry. For a fixed frequency, the angular resolution of a conventional Multiple-Input Multiple-Output (MIMO) radar is limited by the number of physical antennas, and therefore improve the...