Multi-target Radar Detection within a Sparsity Framework

TL;DR

This paper introduces a new framework for multi-target radar detection using sparse reconstruction techniques, specifically LASSO, to improve detection performance in scenes with multiple targets.

Contribution

It develops a novel framework for multi-target radar detection based on sparsity and the Neyman-Pearson criterion, including initial results and validation methods.

Findings

LASSO-based detection reduces false alarms in multi-target scenarios

Simulation results validate the theoretical false alarm probability analysis

Framework opens new research directions in sparse radar detection

Abstract

Traditional radar detection schemes are typically studied for single target scenarios and they can be non-optimal when there are multiple targets in the scene. In this paper, we develop a framework to discuss multi-target detection schemes with sparse reconstruction techniques that is based on the Neyman-Pearson criterion. We will describe an initial result in this framework concerning false alarm probability with LASSO as the sparse reconstruction technique. Then, several simulations validating this result will be discussed. Finally, we describe several research avenues to further pursue this framework.