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Adaptive stopping for fast particle smoothing
School of Computational Science and Engineering, McMaster University.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Division of Signals and Systems, Chalmers University.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2013 (English)In: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE , 2013, 6293-6297 p.Conference paper, Published paper (Refereed)
Abstract [en]

Particle smoothing is useful for offline state inference and parameter learning in nonlinear/non-Gaussian state-space models. However, many particle smoothers, such as the popular forward filter/backward simulator (FFBS), are plagued by a quadratic computational complexity in the number of particles. One approach to tackle this issue is to use rejection-sampling-based FFBS (RS-FFBS), which asymptotically reaches linear complexity. In practice, however, the constants can be quite large and the actual gain in computational time limited. In this contribution, we develop a hybrid method, governed by an adaptive stopping rule, in order to exploit the benefits, but avoid the drawbacks, of RS-FFBS. The resulting particle smoother is shown in a simulation study to be considerably more computationally efficient than both FFBS and RS-FFBS.

Place, publisher, year, edition, pages
IEEE , 2013. 6293-6297 p.
Keyword [en]
Sequential Monte Carlo, particle smoothing, backward simulation
National Category
Signal Processing Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-93461DOI: 10.1109/ICASSP.2013.6638876ISBN: 978-147990356-6 (print)OAI: oai:DiVA.org:liu-93461DiVA: diva2:624920
Conference
38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013; Vancouver, BC; Canada
Projects
CNDMCADICS
Funder
Swedish Research Council, 621-2010-5876
Available from: 2013-06-03 Created: 2013-06-03 Last updated: 2014-12-02

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fulltext(336 kB)329 downloads
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Lindsten, FredrikSchön, Thomas B.
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