Conventional focusing methods for Synthetic Aperture Radar (SAR) employ block processing efficiently but remain latency-heavy processes that prevent the realisation of a closed-loop cognitive SAR vision system. We present the first Online SAR Processor (OSP), an online image-formation framework that treats SAR sensing as a stream and produces focused SAR image output line by line during acquisition. OSP uses a tiny state-space surrogate model trained with teacher-student distillation and multi-stage losses. We evaluate the method on 300GB of SAR data from Maya4, a Sentinel-1-derived dataset containing raw, range-compressed, range-cell-migration-corrected, and azimuth-compressed products. Relative to a linewise digital-signal-processing baseline, OSP delivers approximately 70$\times$ lower latency and 130$\times$ lower memory use; on a single AMD CPU core it processes one row in 16 ms with a memory footprint of 6 MB whilst maintaining a focusing quality high enough to support downstream decisions, which we illustrate with vessel detection and flood-mapping tasks.

翻译：传统合成孔径雷达（SAR）聚焦方法虽能高效处理数据块，但其高延迟特性阻碍了闭环认知SAR视觉系统的实现。本文提出了首个在线SAR处理器（OSP），这是一种将SAR感知视为数据流的在线成像框架，可在采集过程中逐行生成聚焦SAR图像输出。OSP采用经教师-学生蒸馏与多阶段损失训练的小型状态空间替代模型。我们基于Maya4数据集（源自Sentinel-1的300GB SAR数据，包含原始、距离压缩、距离徙动校正及方位压缩产品）对该方法进行了评估。相较于逐行数字信号处理基线，OSP实现了约70倍的延迟降低和130倍的内存占用减少：在单颗AMD CPU核心上，其处理每行数据仅需16毫秒，内存占用6 MB，同时保持足以支撑下游决策的聚焦质量——我们通过船舶检测和洪水制图任务验证了这一点。