Learning to Focus Synthetic Aperture Radar On-line with State-Space Models

TL;DR

This paper introduces an online SAR image processing framework that significantly reduces latency and memory usage, enabling real-time focused SAR imaging during data acquisition.

Contribution

The authors develop the first online SAR processor using a state-space model trained with distillation, achieving real-time processing with high focusing quality.

Findings

70× lower latency compared to baseline

130× lower memory use

Processes one row in 16 ms on a single CPU core

Abstract

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.