{
  "abstract": "Predictively-oriented (PrO) filtering for state-space models was proposed in Shen et al. [2026] as a promising route to avoiding over-confident inferences in settings where either the dynamical model, the measurement model, or both, could be misspecified. Our focus is on nonlinear state-space models, where the local linearisation used to construct the PrO filter in Shen et al. [2026] deteriorates as nonlinearities enter either the dynamical model or the measurement model. Our proposed solution is based on unscented transforms, an established tool from classical filtering which provides a more accurate mechanism to propagate Gaussian uncertainty through a nonlinear functional, and which includes the cubature rule as a special case. We introduce two variants: replacing the linearisation in the predict step alone, which we call UKF-PrO, has a computational cost comparable to the existing PrO filter; replacing it in the update step as well, UKF-PrO+, requires a substantially more expensive joint optimisation. Performance is empirically assessed on the Lorenz96 model under conditions of increasing difficulty, in which the state-space model is systematically misspecified; we find that UKF-PrO with the parameters of the cubature Kalman filter offers consistent improvements, which become substantial under strong nonlinearity, whereas the default unscented parameters are unreliable, and that the full update-step extension with the same parameters does not justify its additional cost.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14773",
  "author_total": 3,
  "authors": [
    {
      "name": "Dan Kean"
    },
    {
      "name": "Zheyang Shen"
    },
    {
      "name": "Chris. J. Oates"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/78p5mh4rh32j7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14773",
  "fulltext_url": null,
  "id": "78p5mh4rh32j7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14773",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Target Tracking and Data Fusion in Sensor Networks"
    ]
  ],
  "openalex": "W7221818526",
  "pdf_url": "https://arxiv.org/pdf/2610.14773",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/78p5mh4rh32j7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2610.14773",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14773"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "An Unscented PrO Kalman Filter",
  "title_text": "An Unscented PrO Kalman Filter",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/78p5mh4rh32j7",
  "venue": "arXiv",
  "versions": []
}