@inproceedings{d1dd50fb0ce24a00b5e12606c8f10772,
title = "Adapting particle filter on interval data for dynamic state estimation",
abstract = "Over the last years, Particle Filters (PF) have attracted considerable attention in the field of nonlinear state estimation due to their relaxation of the linear and Gaussian restrictions in the state space model. However, for some applications, PF are not adapted for a real-time implementation. In this paper we propose a new method, called Box Particle Filter (BPF), for dynamic nonlinear state estimation, which is based on particle filters and interval frameworks and which is well adapted for real time applications. Interval framework will allow to explain regions with high likelihood by a small number of box particles instead of a large number of particles in the case of PF. Experiments on real data for global localization of a vehicle show the usefulness and the efficiency of the proposed approach.",
keywords = "Interval analysis, Mobile robots, Monte Carlo methods, Multisensor systems, State estimation",
author = "Fahed Adallah and Amadou Gning and Philippe Bonnifait",
year = "2007",
doi = "10.1109/ICASSP.2007.366445",
language = "English",
isbn = "1424407281",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1153--1156",
booktitle = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07",
address = "United States",
note = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 ; Conference date: 15-04-2007 Through 20-04-2007",
}