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An iterative implementation of the implicit nonlinear filter

Published online by Cambridge University Press:  11 January 2012

Alexandre J. Chorin
Affiliation:
Department of Mathematics, University of California at Berkeley and Lawrence Berkeley National Laboratory, 970 Evans Hall #3840, Berkeley, 94720-3840 CA, USA. chorin@math.berkeley.edu
Xuemin Tu
Affiliation:
Department of Mathematics, University of Kansas, 405 Snow Hall, 1460 Jayhawk Blvd, Lawrence, 66045-7594 Kansas, USA; xtu@math.ku.edu
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Abstract

Implicit sampling is a sampling scheme for particle filters, designed to move particles one-by-one so that they remain in high-probability domains. We present a new derivation of implicit sampling, as well as a new iteration method for solving the resulting algebraic equations.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2012

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References

Arulampalam, M., Maskell, S., Gordon, N. and Clapp, T., A tutorial on particle filters for online nonlinear/nongaussian Bayesia tracking. IEEE Trans. Signal Process. 50 (2002) 174188. Google Scholar
Bickel, P., Li, B. and Bengtsson, T., Sharp failure rates for the bootstrap particle filter in high dimensions. IMS Collections : Pushing the Limits of Contemporary Statistics : Contributions in Honor of Jayanta K. Ghosh 3 (2008) 318329. Google Scholar
S. Bozic, Digital and Kalman Filtering. Butterworth-Heinemann, Oxford (1994).
Chorin, A.J. and Krause, P., Dimensional reduction for a Bayesian filter. Proc. Natl. Acad. Sci. USA 101 (2004) 1501315017. Google Scholar
Chorin, A.J. and Tu, X., Implicit sampling for particle filters. Proc. Natl. Acad. Sc. USA 106 (2009) 1724917254. Google ScholarPubMed
Chorin, A.J., Morzfeld, M. and Tu, X., Implicit particle filters for data assimilation. Commun. Appl. Math. Comput. Sci. 5 (2010) 221240. Google Scholar
A. Doucet and A. Johansen, Particle filtering and smoothing : Fifteen years later, in Handbook of Nonlinear Filtering, edited by D. Crisan and B. Rozovsky, to appear.
Doucet, A., Godsill, S. and Andrieu, C., On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10 (2000) 197208. Google Scholar
A. Doucet, N. de Freitas and N. Gordon, Sequential Monte Carlo Methods in Practice. Springer, New York (2001).
Dowd, M., A sequential Monte Carlo approach for marine ecological prediction. Environmetrics 17 (2006) 435455. Google Scholar
Gilks, W. and Berzuini, C., Following a moving target-Monte Carlo inference for dynamic Bayesian models. J. Roy. Statist. Soc. B 63 (2001) 127146. Google Scholar
Liu, J. and Sabatti, C., Generalized Gibbs sampler and multigrid Monte Carlo for Bayesian computation. Biometrika 87 (2000) 353369. Google Scholar
Maceachern, S., Clyde, M. and Liu, J., Sequential importance sampling for nonparametric Bayes models : the next generation. Can. J. Stat. 27 (1999) 251267. Google Scholar
M. Morzfeld, X. Tu, E. Atkins and A.J. Chorin, A random map implementation of implicit filters. Submitted to J. Comput. Phys.
Snyder, C., Bengtsson, T., Bickel, P. and Anderson, J., Obstacles to high-dimensional particle filtering. Mon. Weather Rev. 136 (2008) 46294640. Google Scholar