{
  "abstract": "Fine-grained unmanned aerial vehicle (UAV) type classification benefits from complementary sensors, yet real deployments face incomplete observations and heterogeneous stream reliability. We present a missing-aware fusion framework for RGB detections (YOLO11-S), Livox Avia LiDAR, 360° LiDAR, and radar-enhanced point clouds on MMAUD. Confidence–density scores are masked and renormalized so fusion weight is assigned only to observed streams (Eq. (2)); each absent stream still occupies a classifier slot via a learnable missing embedding that is not multiplied by the zero weight (Eq. (1)), keeping absence as a trainable signal rather than a numerical zero. On a time-stratified split, Tiny MLP (≈ 0.1 M fusion parameters) reaches 96.12% test accuracy in a representative seed-42 run. Over seeds 42–46, final test accuracy is 95.29 ± 0.60% without training-time modality dropout and 95.77 ± 0.90% with 30% training ModDrop (mean ± sample s.d.). Under matched 30% test missingness, accuracy remains 83.68% (seed 42). Protocol-aligned baselines and orthogonal ablations support the slot rule. Fusion-stage inference is about 2.1 ms per sample with pre-extracted features on an RTX 4090. Under our fixed split and frozen features—not as a challenge-leaderboard claim—compact missing-aware fusion is practical for heterogeneous UAV sensing under partial sensor availability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Yibo Feng"
    },
    {
      "name": "Ning Li"
    },
    {
      "name": "Di Wu"
    },
    {
      "name": "Ximing Ma"
    },
    {
      "name": "Xiangyue Yu"
    },
    {
      "name": "Haiyang Bao",
      "orcid": "0009-0004-8938-5468"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/84d8rp73prfa1.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76332-z",
  "fulltext_url": null,
  "id": "84d8rp73prfa1",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76332-z",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7221877719",
  "pdf_url": "https://www.nature.com/articles/s41598-026-76332-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/84d8rp73prfa1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "10.1038/s41598-026-76332-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76332-z"
    }
  ],
  "subjects": [],
  "title": "Lightweight multimodal fusion for UAV classification with explicit missing-modality modeling",
  "title_text": "Lightweight multimodal fusion for UAV classification with explicit missing-modality modeling",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:28Z",
  "url": "https://research.draconitic.com/paper/84d8rp73prfa1",
  "venue": "Scientific Reports",
  "versions": []
}