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Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different...
One of the most criticized aspects of particle filtering algorithms is their dependence on model assumptions. However, a rigorous study of the effect of modeling errors on the performance of such algorithms is still missing. In this paper, the problem of using an inaccurate discrete state-space model is considered and a systematic methodology for studying the effects on its performance is proposed...
In this paper we address the problem of applying particle filtering to complex systems. In general, we consider complex systems as ones with nonlinearities and high dimensionality of the state space. We examine strategies for filtering where the state space is partitioned into subspaces and where each subspace is explored by its own particle filter. These particle filters are interconnected and communicate...
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