Riemann–Langevin Particle Filtering in Track-Before-Detect

Fernando Iglesias Garcia (Corresponding Author), Pranab K. Mandal, Melanie Bocquel, Antonio Garcia Marques

    Research output: Contribution to journalArticleAcademicpeer-review

    5 Citations (Scopus)
    50 Downloads (Pure)

    Abstract

    Track-before-detect (TBD) is a powerful approach that consists in providing the tracker directly with the sensor measurements without any predetection. Due to the measurement model nonlinearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in differential-geometric LMC-based filtering to introduce its application to TBD. The benefits of LMC filtering in TBD are illustrated in a challenging low-noise scenario.
    Original languageEnglish
    Pages (from-to)1039-1043
    Number of pages5
    JournalIEEE signal processing letters
    Volume25
    Issue number7
    Early online date28 May 2018
    DOIs
    Publication statusPublished - Jul 2018

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