A survey of convergence results on particle filtering methods for practitioners

Journal article

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.

Authors: Crisan, D; Doucet, A

• Publication status: Published
• Journal: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• Volume: 50
• Issue: 3
• Pages: 736-746
• Publication date: 2002-03-01
• DOI: 10.1109/78.984773
• ISSN: 1053-587X
• Language: English
• Keywords: sequential Monte Carlo; particle filtering; optimal filtering; Bayesian estimation; state-space models