Adaptive Space-Time Beamforming in Radar Systems

TL;DR

This paper reviews recent advances in space-time beamforming algorithms for radar systems, focusing on techniques that exploit low-rank, sparsity, and prior knowledge to enhance performance across various radar configurations.

Contribution

It provides a comprehensive review of recent algorithms and their applications in different radar systems, highlighting the use of low-rank, sparsity, and prior knowledge.

Findings

Enhanced radar detection performance through advanced beamforming techniques

Effective exploitation of low-rank and sparse properties in algorithms

Improved STAP algorithms using prior knowledge

Abstract

The goal of this chapter is to review the recent work and advances in the area of space-time beamforming algorithms and their application to radar systems. These systems include phased-array \cite{melvin} and multi-input multi-output (MIMO) radar systems \cite{haimo_08}, mono-static and bi-static radar systems and other configurations \cite{melvin}. Furthermore, this chapter also describes in detail some of the most successful space-time beamforming algorithms that exploit low-rank and sparsity properties as well as the use of prior-knowledge to improve the performance of STAP algorithms in radar systems.