@inproceedings{6f07f8455e8d43f5869bcdd83ae137e8,
title = "Mixture of uniform probability density functions for non linear state estimation using interval analysis",
abstract = "In this work, a novel approach to nonlinear non-Gaussian state estimation problems is presented based on mixtures of uniform distributions with box supports. This class of filtering methods, introduced in [1] in the light of interval analysis framework, is called Box Particle Filter (BPF). It has been shown that weighted boxes, estimating the state variables, can be propagated using interval analysis tools combined with Particle filtering ideas. In this paper, in the light of the widely used Bayesian inference, we present a different interpretation of the BPF by expressing it as an approximation of posterior probability density functions, conditioned on available measurements, using mixture of uniform distributions. This interesting interpretation is theoretically justified. It provides derivation of the BPF procedures with detailed discussions.",
keywords = "Bayesian Filters, Interval Analysis, Kalman Filters, Monte Carlo Methods, Non linear System, Uniform distribution",
author = "Amadou Gning and Lyudmila Mihaylova and Fahed Abdallah",
year = "2010",
doi = "10.1109/icif.2010.5712085",
language = "English",
isbn = "9780982443811",
series = "13th Conference on Information Fusion, Fusion 2010",
publisher = "IEEE Computer Society",
booktitle = "13th Conference on Information Fusion, Fusion 2010",
address = "United States",
}