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In Gaussian particle filtering the distributions of interest are approximated by Gaussians or mixtures of Gaussians. In this paper, we present an approach for using Gaussian particle filtering in high dimensional systems. The approach is based on breaking the high-dimensional systems into smaller-dimensional systems (subsystems) and applying Gaussian particle filtering in each of the subsystems. The...
Particle filtering methods aim at tracking probability distributions sequentially in time. One of the main challenges of these methods is their accuracy in high-dimensional state spaces. Namely, it can be shown that if the dimensions of these spaces are sufficiently high, the obtained results by particle filtering are practically useless. In this paper, we propose an approach for addressing this problem...
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