A Gauss Library for Particle Filters

December 2008

PF is a Gauss library written with T. Roncalli for computing particle filters using the numerical algorithms described in S. Arulampalam, S. Maskell, N.J. Gordon and T. Clapp [2002], A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transaction on Signal Processing, 50:2, 174-188.

This library contains procedures for computing
— Generic Particle Filter (GPF) algorithm
— Regularized Particle Filter (RPF) algorithm
— Sampling Importance Resampling (SIR) algorithm
— Sampling Importance Sampling (SIS) algorithm
— Particle smoother (PS) algorithm

Download the manual of the Gauss libraryDownload the Gauss library


A Gauss library for Minimax Filters

— Forthcoming
— Hinf is a Gauss library written for the completion of my PhD dissertation. It implements the Minimax filter as described in D. Simon, Optimal State Estimation, Wiley Interscience, Chapters 11-12.