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