Adaptive Radar

Adaptive radar aims to improve target detection and localization by dynamically adjusting to changing environmental conditions. Current research heavily utilizes data-driven approaches, employing convolutional neural networks (CNNs) to process radar data (often visualized as heatmaps) and improve target localization accuracy compared to traditional methods like peak-finding. This involves generating large, realistic datasets simulating diverse environments and leveraging techniques like subspace perturbation analysis to enhance model robustness across various scenarios. The development of such data-driven techniques promises significant advancements in radar technology with applications in various fields requiring precise target identification and tracking.