The probability hypothesis density (PHD) filter is an efficient algorithm for multitarget tracking in the presence of nonlinearities and/or non-Gaussian noise. The sequential Monte Carlo (SMC) and Gaussian mixture (GM) techniques are commonly used to implement the PHD filter. Recently, a new implementation of the PHD filter using B-splines with the capability to model any arbitrary density functions using only a few knots was proposed. The spline PHD (SPHD) filter was found to be more robust than the SMC-PHD filter because it does not suffer from degeneracy, and it was better than the GM-PHD implementation in terms of estimation accuracy, albeit with a higher computational complexity. In this paper, we propose a multiple model extension to the SPHD filter to track multiple maneuvering targets. Simulation results are presented to demonstrate the effectiveness of the new filter.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multiple model spline probability hypothesis density filter


    Beteiligte:


    Erscheinungsdatum :

    01.06.2016


    Format / Umfang :

    1086195 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multiple-model probability hypothesis density filter for tracking maneuvering targets

    Punithakumar, K. / Kirubarajan, T. / Sinha, A. | IEEE | 2008



    Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets

    Han, Y. / Zhu, H. / Han, C. et al. | British Library Online Contents | 2014