This chapter covers the particle filter, which handles severe nonlinearity as well as non‐Gaussianity. Particle filter deploys the sequential Monte Carlo method as a numerical approximation scheme to approximate the corresponding distributions by a set of particles, which are random samples. These samples are drawn from a proposal or importance density, which has the same support as the distribution of interest. A set of normalized weights is associated with the set of particles. The prior, the likelihood, or a Gaussian approximation of the posterior, which is provided by the extended Kalman filter or the unscented Kalman filter, can be selected as the proposal distribution. Resampling is used to address the degeneracy problem in the sequential importance sampling. To cope with the sample impoverishment problem due to resampling, regularization and resample‐move algorithm can be deployed. Simultaneous localization and mapping is reviewed as an application of particle filtering algorithms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Particle Filter


    Contributors:

    Published in:

    Publication date :

    2022-04-12


    Size :

    13 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Gaussian Diffracted Particle Filter

    Ning, X. / Wang, H. / Ning, Y. | British Library Online Contents | 2010


    Brake dust particle filter with filter element

    WALZ THOMAS / VEREL BERTRAND / BU CK LUDGER et al. | European Patent Office | 2022

    Free access

    Quadrature Kalman Particle Filter

    Wu, C. / Han, C. | British Library Online Contents | 2009


    Distributed Particle Filter Based on Particle Exchanges

    Tang, Rui / Riemens, Ellen / Rajan, Raj Thilak | IEEE | 2023


    Brake Dust Particle Filter, Lighting Housing Part for a Brake Dust Particle Filter, and Vehicle with Brake Dust Particle Filter

    KUEMMERLING VOLKER / JESSBERGER THOMAS / BOCK LUKAS et al. | European Patent Office | 2021

    Free access