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Related Experiment Video

Updated: Jun 17, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

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

Ghalia Nassreddine¹, Fahed Abdallah, Thierry Denoux

¹UMR CNRS 6599 HEUDIASYC, Université de Technologie de Compiègne, 60205 Compiègne Cedex, France. gnassred@hds.utc.fr

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|December 17, 2009

Summary

This study introduces a novel nonlinear state estimation method using belief functions and interval analysis for accurate vehicle localization. It offers guaranteed computations and improved accuracy over existing methods.

Related Experiment Videos

Last Updated: Jun 17, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Area of Science:

Engineering
Computer Science
Mathematics

Background:

Nonlinear state estimation is crucial for applications like vehicle localization.
Existing methods like bounded-error and probabilistic approaches have limitations in handling uncertainties.
Accurate representation of partial information on uncertainties is needed.

Purpose of the Study:

To present a new nonlinear state estimation approach using belief-function theory and interval analysis.
To improve the accuracy and reliability of vehicle localization.
To provide guaranteed computations while handling uncertainties effectively.

Main Methods:

Utilizing belief structures represented by axis-aligned boxes with associated masses.
Propagating focal sets in system equations via interval arithmetics and constraint-satisfaction.
Applying the method to land vehicle localization using Global Positioning System (GPS) and dead reckoning sensors.

Main Results:

The proposed method provides more accurate vehicle position estimates than the bounded-error approach.
It retains the essential feature of guaranteed computations.
Performance was comparable to particle filters with similar running times.

Conclusions:

The new method is a viable alternative for vehicle localization.
It offers advantages over both bounded-error and probabilistic Monte Carlo methods.
It accurately represents partial information on model and measurement uncertainties.