A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Nested Superpositional Measurements

[2602.02365] A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Nested Superpositional Measurements Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Signal Processing arXiv:2602.02365 (eess) [Submitted on 2 Feb 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Nested Superpositional Measurements Authors: Sion Lynch , Ángel F. García-Fernández , Lee Devlin View PDF HTML (experimental) Abstract: This paper proposes the Trajectory-Information Exchange Multi-Bernoulli (T-IEMB) filter to estimate sets of alive and all trajectories in track-before-detect applications with nested superpositional measurements. This measurement model has a nested architecture with two layers. The first layer includes superpositional hidden variables which are then mapped in the second layer to the conditional mean and covariance of the measurement, enabling it to model a broad range of measurement models. This paper also presents a Gaussian implementation of the T-IEMB filter, which performs the update by approximating the conditional moments of the measurement model, a computationally light filtering solution. Simulation results for a non-Gaussian radar-based tracking scenario demonstrate the performance of two Gaussian T-IEMB implementations, which provide improved tracking performance compared to state-of-the-art particle filters for track-before-detect, at a reduced computational cost. Comments: Python code is available at this https URL Subjects: Signal Processing (eess.SP) Cite as: arXiv:2602.02365 [eess.SP] (or arXiv:2602.02365v2 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2602.02365 Focus to learn more arXiv-issued DOI via DataCite Journal reference: S. Lynch, Á. F. García-Fernández and L. Devlin, "A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Nested Superpositional Measurements," in IEEE Transactions on Signal Processing, vol. 74, pp. 3503-3519, 2026 Related DOI : https://doi.org/10.1109/TSP.2026.3719138 Focus to learn more DOI(s) linking to related resources Submission history From: Sion Lynch [ view email ] [v1] Mon, 2 Feb 2026 17:24:15 UTC (522 KB) [v2] Thu, 27 Aug 2026 08:30:10 UTC (199 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-02 Change to browse by: eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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