Backward sequential Monte Carlo for marginal smoothing

In this paper we propose a new type of particle smoother with linear computational complexity. The smoother is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. While this introduces dependencies among the backward trajectories we show throug...

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Bibliographic Details
Main Authors: Kronander, Joel, Schon, Thomas B., Dahlin, Johan
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Approximation methods
Forward-backward algorithms
Monte Carlo methods
Particle filter
Particle smoother
Runtime
Sequential Monte Carlo
Signal processing
Signal processing algorithms
Smoothing methods
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Description
Summary:In this paper we propose a new type of particle smoother with linear computational complexity. The smoother is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. While this introduces dependencies among the backward trajectories we show through simulation studies that the new smoother can outperform existing forward-backward particle smoothers when targeting the marginal smoothing densities.
ISSN:2373-0803
2693-3551
DOI:10.1109/SSP.2014.6884652