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Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the filter is guaranteed when the number of particles tends to infinity, the quality of the approximation is usually unknown but strongly dependent on the number of particles. In this paper, we propose a novel method for assessing...
In this paper, we propose a novel approach for assessing the convergence of particle filters in online manner. Particle filters sequentially approximate distributions of hidden states of state-space models. The approximations are random measures composed of weighted particles (i.e., samples of the state). A sufficiently large number of particles provides a good quality in the approximation but at...
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