Real-Time Particle Filters

Particle filters estimate the state of dynamical systems from sensor infor- mation. 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 fil- ters, which make use of all sensor information even when the filter update rate is below the update rate of the sensors. This is achieved by represent- ing posteriors as mixtures of sample sets, where each mixture component integrates one observation arriving during a filter update.