We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a finite-dimensional filter with arbitrarily good accuracy. The validity of the conceptual solution is proved via simulations on a one dimensional tracking problem and implementation issues are discussed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Non-linear filtering based on observations from Gaussian processes


    Contributors:
    Gustafsson, F (author) / Saha, S (author) / Orguner, U (author)

    Published in:

    Publication date :

    2011-03-01


    Size :

    1086578 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Non-linear filtering based on observations from Student's t processes

    Saha, Saikat / Orguner, Umut / Gustafsson, Fredrik | IEEE | 2012



    Driver-Gaze Zone Estimation Using Bayesian Filtering and Gaussian Processes

    Lundgren, Malin / Hammarstrand, Lars / McKelvey, Tomas | IEEE | 2016


    Quadratic filtering of non-Gaussian linear systems with random observation matrices

    Cacae,F. / Fasano,A. / Germani,A. et al. | Automotive engineering | 2014