{
  "abstract": "Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14516",
  "author_total": 5,
  "authors": [
    {
      "name": "Keying Zhu"
    },
    {
      "name": "Le Liang"
    },
    {
      "name": "Jie Yang"
    },
    {
      "name": "Fan Liu"
    },
    {
      "name": "Shi Jin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/530nhpeydj12c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14516",
  "fulltext_url": null,
  "id": "530nhpeydj12c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14516",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2202|Advanced SAR Imaging Techniques"
    ]
  ],
  "openalex": "W7221799869",
  "pdf_url": "https://arxiv.org/pdf/2610.14516",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/530nhpeydj12c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.14516",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14516"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems",
  "title_text": "UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/530nhpeydj12c",
  "venue": "arXiv",
  "versions": []
}