A survey of convergence results on particle filtering methods for practitioners

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...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2002-03, Vol.50 (3), p.736-746
Main Authors: Crisan, D., Doucet, A.
Format: Article
Language:English
Subjects:
Applied sciences
Bayesian methods
Closed-form solution
Convergence
Design methodology
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering
Filtering algorithms
Filters
Filtration
Hidden Markov models
Information, signal and communications theory
Mathematical models
Monte Carlo methods
Optimization
Samples
Signal and communications theory
Signal processing
Signal, noise
State estimation
Statistical analysis
Telecommunications and information theory
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.984773