arXiv:2605.10340v2 Announce Type: replace Abstract: Conventional focusing methods for Synthetic Aperture Radar (SAR) employ block-wise processing efficiently but remain latency-heavy processes that prevent the realisation of a closed-loop cognitive SAR system. We present an online SAR image-formation framework, StreamAZ, that treats sensing as a stream and produces focused images in a line by line fashion. Our method uses a tiny state-space surrogate model trained with teacher-student distillation and multi-stage losses. We evaluate the method on real 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, our framework 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.