{
  "abstract": "Acoustic telemetry is a core methodology for collecting fine-scale movement data for aquatic animals. When telemetry receivers are set up in closely spaced arrays with overlapping detection ranges, the detection times of a tagged animal can be used to estimate its position and movement paths. In practice, estimating these paths can be challenging. Traditional time-difference-of-arrival methods generally provide positioning accuracy too poor for inferring fine-scale behaviours, while more robust state-space positioning models can be computationally intensive and practically infeasible to run on large datasets. Here, a novel telemetry positioning method is presented where time-of-arrival positioning is implemented as a state-space model within an extended Kalman filter. The resulting model, termed EK-TOA, provides closed-form solutions to track estimation. Simulated datasets of fish movement within a 2D telemetry array are used to verify the models performance and a real case study is provided where EK-TOA is utilized for the long-term tracking of a tagged fish. In comparison to currently available positioning models, EK-TOA provides a fast and accurate solution for tracking fine-scale movement behaviours of aquatic animals over long, continuous periods of time.",
  "abstract_shown": true,
  "affiliations": [
    "Leibniz Institute of Freshwater Ecology and Inland Fisheries"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. A. Campbell"
    },
    {
      "name": "J. Elings"
    },
    {
      "name": "P. Lundberg"
    },
    {
      "name": "R. Mawer"
    },
    {
      "name": "I. Pauwels"
    },
    {
      "name": "F. Hölker"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a6dvnajak2qr5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.17.752304",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752304v1.full",
  "id": "a6dvnajak2qr5",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752304v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "ecology"
    ],
    [
      "openalex",
      "1702|Target Tracking and Data Fusion in Sensor Networks"
    ]
  ],
  "openalex": "W7213537559",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752304v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a6dvnajak2qr5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:05Z",
      "native_id": "10.64898/2026.09.17.752304",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752304v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "An extended Kalman filter for large-volume path positioning of aquatic animals within acoustic telemetry arrays",
  "title_text": "An extended Kalman filter for large-volume path positioning of aquatic animals within acoustic telemetry arrays",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:05Z",
  "url": "https://research.draconitic.com/paper/a6dvnajak2qr5",
  "venue": "bioRxiv",
  "versions": []
}