This paper is devoted to the problem of multitarget tracking with nonlinear models and uncertain noise statistics in the framework of random finite sets. Based on the linear approximation strategy, a robust closed-form solution to the probability hypothesis density recursion is proposed in terms of an H norm minimization criterion. This idea is extended to develop another analytic implementation using the unscented transform technique. Simulations using the proposed approach are also presented.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Nonlinear Gaussian mixture phd filter with an H∞ criterion


    Contributors:
    Wenling Li (author) / Yingmin Jia (author)


    Publication date :

    2016-08-01


    Size :

    812671 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Gaussian Mixture Approximation by Another Gaussian Mixture for `Blob' Filter Re-Sampling

    Psiaki, M. / Schoenberg, J. / Miller, I. et al. | British Library Conference Proceedings | 2010


    Gaussian Mixture Approximation by Another Gaussian Mixture for "Blob" Filter Re-Sampling

    Psiaki, Mark / Schoenberg, Jonathan / Miller, Isaac | AIAA | 2010


    Nonlinear Gaussian Mixture Filtering with Intrinsic Fault Resistance

    Fritsch, Gunner S. / DeMars, Kyle J. | AIAA | 2021


    A Gaussian Mixture Extended-Target Multi-Bernoulli Filter

    Zhang, G. / Lian, F. / Han, C. et al. | British Library Online Contents | 2014


    Automated Splitting Gaussian Mixture Nonlinear Measurement Update

    Tuggle, Kirsten / Zanetti, Renato | AIAA | 2018