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This paper introduces an online edge-mapping technique for SAR ATR that operates directly on back-scattered signals, bypassing traditional image reconstruction. By generating sparse edge maps, the method reduces memory and computational demands compared to backprojection. The proposed approach enables efficient onboard ATR processing for resource-constrained UAVs in defense applications.
Ditch bulky SAR image reconstruction: this online edge-mapping technique slashes memory and compute costs for UAV-based target recognition.
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.
