State estimation using interval analysis and belief-function theory: application to dynamic vehicle localization

Authors:
Ghalia Nassreddine;Fahed Abdallah;Thierry Denœux
Affiliations:
UMR, CNRS, HEUDIASYC, Université de Technologie de Compiègne, Compiègne Cedex, France;UMR, CNRS, HEUDIASYC, Université de Technologie de Compiègne, Compiègne Cedex, France;UMR, CNRS, HEUDIASYC, Université de Technologie de Compiègne, Compiègne Cedex, France
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2010

Citing 16
Cited 4

Arithmetic and other operations on Dempster-Shafer structures

International Journal of Man-Machine Studies
Constraint propagation with interval labels

Artificial Intelligence
The transferable belief model

Artificial Intelligence
Fuzzy sets as a basis for a theory of possibility

Fuzzy Sets and Systems
Inner and outer approximation of belief structures using a hierarchical clustering approach

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)

Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Kalman filter and joint tracking and classification based on belief functions in the TBM framework

Information Fusion
Brief Paper: Box particle filtering for nonlinear state estimation using interval analysis

Automatica (Journal of IFAC)
Practical representations of incomplete probabilistic knowledge

Computational Statistics & Data Analysis
Classic Works of the Dempster-Shafer Theory of Belief Functions

Classic Works of the Dempster-Shafer Theory of Belief Functions
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking

IEEE Transactions on Signal Processing
Cumulative distribution functions from Dempster-Shafer belief structures

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Technical Communique: Nonlinear bounded-error state estimation of continuous-time systems

Automatica (Journal of IFAC)
Constraints propagation techniques on intervals for a guaranteed localization using redundant data

Automatica (Journal of IFAC)

Set-membership localization with probabilistic errors

Robotics and Autonomous Systems
Particle filtering in the Dempster--Shafer theory

International Journal of Approximate Reasoning
Interval analysis on non-linear monotonic systems as an efficient tool to optimise fresh food packaging

Computers and Electronics in Agriculture
Dempster Shafer neural network algorithm for land vehicle navigation application

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new approach to nonlinear state estimation based on belief-function theory and interval analysis is presented. This method uses belief structures composed of a finite number of axis-aligned boxes with associated masses. Such belief structures can represent partial information on model and measurement uncertainties more accurately than can the bounded-error approach alone. Focal sets are propagated in system equations using interval arithmetics and constraint-satisfaction techniques, thus generalizing pure interval analysis. This model was used to locate a land vehicle using a dynamic fusion of Global Positioning System measurements with dead reckoning sensors. The method has been shown to provide more accurate estimates of vehicle position than does the bounded-error method while retaining what is essential: providing guaranteed computations. The performances of our method were also slightly better than those of a particle filter, with comparable running time. These results suggest that our method is a viable alternative to both bounded-error and probabilistic Monte Carlo approaches for vehicle-localization applications.