Online CS-based SAR Edge-Mapping

TL;DR

This paper introduces an online edge-mapping method for SAR that classifies scenes directly from measurements, reducing computational load and memory requirements for UAV-based target recognition.

Contribution

It proposes a novel online edge-mapping technique that bypasses image reconstruction, promoting sparsity and efficiency in SAR processing for UAV applications.

Findings

Reduces memory and computational requirements compared to traditional SAR methods.

Enables onboard scene and target classification without full image reconstruction.

Promotes sparsity by reconstructing scene edges instead of images.

Abstract

With modern defense applications increasingly relying on inexpensive, small Unmanned Aerial Vehicles (UAVs), a major challenge lies in designing intelligent and computationally efficient onboard Automatic Target Recognition (ATR) algorithms to carry out operational objectives. This is especially critical in Synthetic Aperture Radar (SAR), where processing techniques such as ATR are often carried out post data collection, requiring onboard systems to bear the memory burden of storing the back-scattered signals. To alleviate this high cost, we propose an online, direct, edge-mapping technique which bypasses the image reconstruction step to classify scenes and targets. Furthermore, by reconstructing the scene as an edge-map we inherently promote sparsity, requiring fewer measurements and computational power than classic SAR reconstruction algorithms such as backprojection.