Proceedings of the IEEE, 92(2), 2004
Special Issue on Sequential State Estimation
Abstract
Particle filters estimate the state of dynamic systems from sensor
information. In many real time applications of particle filters,
however, sensor information arrives at a significantly higher rate
than the update rate of the filter. The prevalent approach to
dealing with such situations is to update the particle filter as
often as possible and to discard sensor information that cannot be
processed in time. In this paper we present real-time particle
filters, which make use of \emph{all} sensor information even when
the filter update rate is below the update rate of the sensors.
This is achieved by representing posteriors as mixtures of sample
sets, where each mixture component integrates one observation
arriving during a filter update. The weights of the mixture
components are set so as to minimize the approximation error
introduced by the mixture representation. Thereby, our approach
focuses computational resources on valuable sensor information.
Experiments using data collected with a mobile robot show that our
approach yields strong improvements over other approaches.