Hierarchical fusion in particle filtering track-before-detect

Fernando Iglesias Garcia, Pranab K. Mandal, Melanie Bocquel, Hans Driessen

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    1 Citation (Scopus)


    Track fusion is the problem of combining tracks based on different sensor observations. In the sequential Monte Carlo framework, track fusion is solved by either imposing linear or Gaussian assumptions, or relying on kernel density estimation (KDE). In this paper, we introduce a novel track fusion algorithm suited to the hierarchical multi-sensor architecture. The algorithm can be incorporated in the particle filtering framework without restricting the densities by imposing assumptions, or requiring the non-trivial selection of additional parameters, as e.g., is needed in KDE. Furthermore, the proposed method is equivalent to the optimal centralised fusion architecture, in which all sensor measurements are communicated to the fusion node. Numerical results show that the newly proposed method outperforms the existing methods either by reducing estimation errors or by reducing the computation time significantly.
    Original languageUndefined
    Title of host publicationProceedings of the 19th International Conference on Information Fusion (ISIF 2016)
    Place of PublicationUnited States
    Number of pages7
    ISBN (Print)978-0-9964-5274-8
    Publication statusPublished - 5 Aug 2016
    Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany, Heidelberg, Germany
    Duration: 5 Jul 20168 Jul 2016
    Conference number: 19

    Publication series



    Conference19th International Conference on Information Fusion, FUSION 2016
    Abbreviated titleFUSION 2016
    Other5-8 July 2016


    • EWI-27224
    • Multi-sensor fusion
    • METIS-318521
    • IR-101454
    • particle filtering

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