用状态空间模型实现实时合成孔径雷达成像，处理速度提升70倍。
Learning to Focus Synthetic Aperture Radar On-line with State-Space Models
- 将SAR成像转为流式处理，逐行实时生成聚焦图像
- 单核CPU每行处理仅需16毫秒，内存占用6MB，延迟降为原来的1/70
- 适合需要快速响应的雷达感知系统，如海上船只检测与洪水测绘
传统合成孔径雷达（SAR）聚焦方法采用块处理，虽高效但延迟高，难以支持闭环认知式SAR视觉系统。本文提出首个在线SAR处理器（OSP），将SAR感知视为数据流，边采集边逐行生成聚焦图像。OSP采用通过教师-学生蒸馏和多阶段损失训练的小型状态空间代理模型。在300GB的Maya4数据集上评估，该数据集源自哨兵-1，包含原始数据、距离压缩、距离单元迁移校正及方位压缩产物。相比逐行数字信号处理基线，OSP实现约70倍的延迟降低和130倍的内存减少；在单个AMD CPU核心上每行处理耗时16毫秒，内存占用仅6MB，且聚焦质量足以支撑下游任务决策，已在船只检测和洪水制图任务中得到验证。
原文摘要 · Abstract (English)
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.
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