The problem of reconstruction of random Gaussian fields is investigated taking into consideration the character of basis functions. It's usual to concentrate on representing signals as weighted sums of complex exponential functions. Here we're going to study the more general case of linear combinations of any basis functions taking into account the conditional mean rule as the proposed method to analyses them. With this method is possible to investigate the basis function at the output of different low-pass reconstruction filters. For simplicity it is considered here two low-pass filters: the RC circuit and the two RC circuits in series. On the basis of this rule the reconstruction of random fields is described on the whole space domain. We apply the conditional mean function in order to obtain the reconstruction surface and the conditional variance function to describe the error reconstruction surfaces.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    The Basis Functions in the Sampling-Reconstruction Procedure of Gaussian Random Fields


    Beteiligte:


    Erscheinungsdatum :

    01.11.2013


    Format / Umfang :

    513956 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Maximum Error Estimation of Gaussian Processes in the Sampling-Reconstruction Procedure

    Morales-Arenas, Gabriela / Rodriguez-Saldana, Daniel / Kazakov, Vladimir | IEEE | 2015


    Error Functions of Gaussian Fields Using Radial and Spiral Sampling

    Kazakov, Vladimir / Mendez, Luis / Saldana, Daniel Rodriguez | IEEE | 2013



    Adaptive Tensor Filtration of Gaussian Random Fields

    Perov, A. I. | British Library Online Contents | 1996


    Curb reconstruction using Conditional Random Fields

    Siegemund, J / Pfeiffer, D / Franke, U et al. | IEEE | 2010