12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Mixture of Uniform Probability Density Function...
View graph of relations

« Back

Mixture of Uniform Probability Density Functions for non Linear State Estimation using Interval Analysis.

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date28/07/2010
Host publication13th Conference on Information Fusion (FUSION), 2010
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Print)978-0-9824438-1-1
Original languageEnglish

Conference

Conference13th International Conference on Information Fusion
CityEdinburgh, UK
Period26/07/1029/07/10

Conference

Conference13th International Conference on Information Fusion
CityEdinburgh, UK
Period26/07/1029/07/10

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.

Bibliographic note

Catalogue number: CFP10FUS-CDR ISBN:978-0-9824438-1-1