An extension is presented to the particle filtering toolbox that enables nonlinear/non-Gaussian filtering to be performed in the presence of out-of-sequence measurements (OOSMs) with arbitrary lag, without the need to adopt linearising approximations in the filter and without the degradation of performance that would occur if the OOSMs were simply discarded. An estimate of the performance of the OOSM particle filter (OOSM-PF) is obtained for bearings-only tracking scenarios with a single target and a small number of sensors. These performance estimates are then compared with the posterior Cramer-Rao lower bound (CRLB) for the state estimate rms error and similar performance estimates obtained from the oosm extended Kalman filter (OOSM-EKF) algorithms recently introduced in the literature. For a mildly nonlinear bearings-only tracking problem the OOSM-PF and OOSM-EKF are shown to achieve broadly similar performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Particle filters for tracking with out-of-sequence measurements


    Contributors:
    Orton, M. (author) / Marrs, A. (author)


    Publication date :

    2005-04-01


    Size :

    275786 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Robust vehicle tracking with particle filters

    Gosda, U. / Jentschel, H.J. | Tema Archive | 2008


    Out-of-sequence measurement processing for tracking ground target using particle filters

    Mallick, M. / Kirubarajan, T. / Arulampalam, S. | IEEE | 2002


    Underwater Multi-Target Tracking with Particle Filters

    Masmitja, I. / Gomariz, S. / Del Rio, J. et al. | IEEE | 2018


    6.0504 Out-of-Sequence Measurement Processing for Tracking Ground Target Using Particle Filters

    Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2002