On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Energy consumption prediction using sequential Monte Carlo methods
International Journal of Modelling and Simulation
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In recent years, there has been exciting advances in estimation methods based on Monte Carlo techniques. The particle filtering technique may cope with non-linear models as well as non-Gaussian dynamic and observation noises. It recursively constructs the conditional probability density of the state variables, with respect to all available measurements, through a random exploration of the state space by entities called particles. A weight is assigned to each particle by a Bayesian correction term based on measurements. The main drawback of this procedure is due to the large number of particles needed which limits its application to on-line filtering. A data parallel algorithm is proposed to achieve real-time particle filtering. Extensive results are presented. They show that the accuracy of the method is preserved and that the computing times of the parallel algorithm are compatible with the real-time constraints of the most challenging applications.