{
  "annotation_id": "5468eb56-60aa-47dc-a590-1f76ef68d3ea",
  "date_created": "2026-02-17T05:53:12.310000Z",
  "date_modified": "2026-02-17T05:53:12.310000Z",
  "file_hash": "d61d7db6781407b82b131c9bf8406e9b94e8148c990c3859ffae2fcdd3722957",
  "private": false,
  "record": {
    "abstract": "This study presents an effective autofocusing approach for synthetic aperture radar imaging of the human body under conditions of respiratory motion. The proposed method suppresses respiratory-motion-induced phase errors by separating radar echoes in the spatial- and time-frequency domains and estimating phase errors individually for each separated echo. By compensating for the estimated phase errors, synthetic aperture radar images focused on all scattering points are generated, even when multiple body parts exhibit different motions due to respiration. The performance of the proposed method is evaluated through experiments with four participants in the supine position. Compared with a conventional method, the proposed approach improves image quality by a factor of 5.1 in terms of Muller-Buffington sharpness, and reduces the root-mean-square error with respect to a reference point cloud from 34 mm to 20 mm.",
    "arxiv_id": "2601.07099",
    "authors": [
      "Masaya Kato",
      "Takuya Sakamoto"
    ],
    "categories": [
      "eess.SP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Autofocus Method for Human-Body Imaging under Respiratory Motion Using Synthetic Aperture Radar",
    "url": "https://arxiv.org/abs/2601.07099",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c6c8d940-92af-4edd-ba44-e45bb27927ce",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}