Learning to Focus Synthetic Aperture Radar On-line with State-Space Models
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916000326221824 |
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| author | Fieldhouse, Sebastian Del Prete, Roberto Daga, Gabriele Rensly, Nathaniel Meoni, Gabriele Tang, Kea-Tiong |
| author_facet | Fieldhouse, Sebastian Del Prete, Roberto Daga, Gabriele Rensly, Nathaniel Meoni, Gabriele Tang, Kea-Tiong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10340 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning to Focus Synthetic Aperture Radar On-line with State-Space Models Fieldhouse, Sebastian Del Prete, Roberto Daga, Gabriele Rensly, Nathaniel Meoni, Gabriele Tang, Kea-Tiong Image and Video Processing Computational Engineering, Finance, and Science Emerging Technologies 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. |
| title | Learning to Focus Synthetic Aperture Radar On-line with State-Space Models |
| topic | Image and Video Processing Computational Engineering, Finance, and Science Emerging Technologies |
| url | https://arxiv.org/abs/2605.10340 |