Online CS-based SAR Edge-Mapping
This addresses the problem of high memory and computational costs for SAR processing in defense applications using UAVs, representing an incremental improvement by focusing on edge-mapping rather than full reconstruction.
The paper tackles the challenge of computationally efficient onboard Automatic Target Recognition (ATR) for Synthetic Aperture Radar (SAR) on small UAVs by proposing an online edge-mapping technique that bypasses image reconstruction, resulting in reduced memory and computational requirements compared to classic methods like backprojection.
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.