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A survey of convergence results on particle filtering methods for practitioners

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2 Author(s)
Crisan, D. ; Dept. of Math., Imperial Coll. of Sci., Technol. & Med., London, UK ; Doucet, Arnaud

Optimal filtering problems are ubiquitous in signal processing and related fields. Except for a restricted class of models, the optimal filter does not admit a closed-form expression. Particle filtering methods are a set of flexible and powerful sequential Monte Carlo methods designed to. solve the optimal filtering problem numerically. The posterior distribution of the state is approximated by a large set of Dirac-delta masses (samples/particles) that evolve randomly in time according to the dynamics of the model and the observations. The particles are interacting; thus, classical limit theorems relying on statistically independent samples do not apply. In this paper, our aim is to present a survey of convergence results on this class of methods to make them accessible to practitioners

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Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 3 )