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Activity Number: 621
Type: Invited
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #307248
Title: Augmented Particle Filters
Author(s): Yuguo Chen*+
Companies: University of Illinois at Urbana-Champaign
Keywords: Nonlinear filtering ; Particle filter ; Sequential Monte Carlo ; State space model
Abstract:

We describe a new particle filtering algorithm, called the augmented particle filter (APF), for online filtering problems in state space models. The APF combines information from both the observation equation and the state equation, and the state space is augmented to facilitate the weight computation. Theoretical justification of the APF is provided, and the connection between the APF and the optimal particle filter in some special state space models is investigated. We apply the APF to several examples to demonstrate the effectiveness of the method.


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