Gaussian recursive filters (RFs) are frequently used in several research fields with th aim to approximate in an efficient way Gaussian filters and Gaussian-based convolutions. Among them, the first-order Gaussian RF, also in its K-iterated form, has been recently used in data assimilation. However, a recent study has proved that in the base case (K = 1) this method is not able to well approximate the Gaussian convolution for all values of the standard deviation. Here we propose a new way to construct a second order RF whose smoothing coefficients are chosen in order to enhance the accuracy of the approximation.

On the Construction of a Second Order Gaussian Recursive Filter

GALLETTI, Ardelio;GIUNTA, Giulio
2016-01-01

Abstract

Gaussian recursive filters (RFs) are frequently used in several research fields with th aim to approximate in an efficient way Gaussian filters and Gaussian-based convolutions. Among them, the first-order Gaussian RF, also in its K-iterated form, has been recently used in data assimilation. However, a recent study has proved that in the base case (K = 1) this method is not able to well approximate the Gaussian convolution for all values of the standard deviation. Here we propose a new way to construct a second order RF whose smoothing coefficients are chosen in order to enhance the accuracy of the approximation.
2016
9781509056989
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/59717
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? ND
social impact