Parallel particle filtering
In recent years, there has been exciting advances in estimation methods based on Monte Carlo techniques. The particle filtering technique may Core 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. Them shove that the accuracy of the method is preserved and that the computing times of the parallel algorithm are compatible kith the real-time constraints of the most challenging applications. (C) 2002 Elsevier Science (USA).
particle filtering parallel algorithm monte carlo methods signal processing