This paper compares state estimation techniques for nonlinear stochastic dynamic systems, which are important for target tracking. Recently, several methods for nonlinear state estimation have appeared utilizing various random-point-based approximations for global filters (e.g., particle filter and ensemble Kalman filter) and local filters (e.g., Monte-Carlo Kalman filter and stochastic integration filters). A special emphasis is placed on derivations, algorithms, and commonalities of these filters. All filters described are put into a common framework, and it is proved that within a single iteration, they provide asymptotically equivalent results. Additionally, some deterministic-point-based filters (e.g., unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter) are shown to be special cases of a random-point-based filter. The paper demonstrates and compares the filters in three examples, a random variable transformation, re-entry vehicle tracking, and bearings-only tracking. The results show that the stochastic integration filter provides better accuracy than the Monte-Carlo Kalman filter and the ensemble Kalman filter with lower computational costs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Random-point-based filters: analysis and comparison in target tracking


    Contributors:


    Publication date :

    2015-04-01


    Size :

    1451635 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Optimization of point target tracking filters

    Caefer, C.E. / Silverman, J. / Mooney, J.M. | IEEE | 2000


    PAPERS - Optimization of Point Target Tracking Filters

    Caefer, C.E. | Online Contents | 2000


    Comparison of several space target tracking filters

    Chen, Huimin / Chen, Genshe / Blasch, Erik et al. | SPIE | 2009


    Target tracking filters

    Lavender, W.A. | Tema Archive | 1974