A survey of convergence results on particle filtering methods for practitioners

نویسندگان

  • Dan Crisan
  • Arnaud Doucet
چکیده

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 recent convergence results on this class of methods to make them accessible to practitioners.

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عنوان ژورنال:
  • IEEE Trans. Signal Processing

دوره 50  شماره 

صفحات  -

تاریخ انتشار 2002