An iterated filtering method is presented to improve the update stage of nonlinear filtering. First, we develop a generalized iterative framework for sigma point kalman filter by utilizing Gauss-Newton algorithm. A simplified iterated square root cubature kalman filter (SCKF) is proposed with application to tightly coupled GNSS/INS, where the linear combination of innovations is used in measurement update. Numerical experiment and field test results indicate that SCKF has similar performance with extended kalman filter. Compared with the non-iterated methods, iterated SCKF improves the heading by 23.6% with two iterations, and get a faster convergence rate regarding heading and velocity.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Iterated square root cubature kalman filter with application to tightly coupled GNSS/INS


    Contributors:
    Bingbo Cui (author) / Haoqian Huang (author) / Xiyuan Chen (author)


    Publication date :

    2016-08-01


    Size :

    407106 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Iterated Square Root Unscented Kalman Filter

    Cheng, L. / Xie, K. | British Library Online Contents | 2008



    Attitude estimation based on quaternion square-root cubature Kalman filter

    Huaming, Q. / Wei, H. / Lei, G. et al. | British Library Online Contents | 2013


    Robust square-root cubature Kalman filter based on Huber’s M-estimation methodology

    Li, Kailong / Hu, Baiqing / Chang, Lubin et al. | SAGE Publications | 2015