Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Mixture of Uniform Probability Density Functions for non Linear State Estimation using Interval Analysis.
AU - Gning, A.
AU - Mihaylova, L.
AU - Abdallah, F.
N1 - Catalogue number: CFP10FUS-CDR ISBN:978-0-9824438-1-1
PY - 2010/7/28
Y1 - 2010/7/28
N2 - 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.
AB - 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.
KW - Non linear System
KW - Bayesian Filters
KW - Uniform distribution
KW - Monte Carlo Methods
KW - Kalman Filters
KW - Interval Analysis
M3 - Conference contribution/Paper
SN - 978-0-9824438-1-1
SP - 1
EP - 8
BT - 13th Conference on Information Fusion (FUSION), 2010
PB - IEEE
T2 - 13th International Conference on Information Fusion
Y2 - 26 July 2010 through 29 July 2010
ER -