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State estimation using the particle filter with mode tracking

Pocock, J. A., Dance, S. L. and Lawless, A. S. (2011) State estimation using the particle filter with mode tracking. Computers & Fluids, 46 (1). pp. 392-397. ISSN 0045-7930

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To link to this article DOI: 10.1016/j.compfluid.2011.02.009

Abstract/Summary

A particle filter is a data assimilation scheme that employs a fully nonlinear, non-Gaussian analysis step. Unfortunately as the size of the state grows the number of ensemble members required for the particle filter to converge to the true solution increases exponentially. To overcome this Vaswani [Vaswani N. 2008. IEEE Trans Signal Process 56:4583–97] proposed a new method known as mode tracking to improve the efficiency of the particle filter. When mode tracking, the state is split into two subspaces. One subspace is forecast using the particle filter, the other is treated so that its values are set equal to the mode of the marginal pdf. There are many ways to split the state. One hypothesis is that the best results should be obtained from the particle filter with mode tracking when we mode track the maximum number of unimodal dimensions. The aim of this paper is to test this hypothesis using the three dimensional stochastic Lorenz equations with direct observations. It is found that mode tracking the maximum number of unimodal dimensions does not always provide the best result. The best choice of states to mode track depends on the number of particles used and the accuracy and frequency of the observations.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
Faculty of Science > School of Mathematical and Physical Sciences > National Centre for Earth Observation (NCEO)
Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics
ID Code:24147
Uncontrolled Keywords:Particle filter; Mode tracking; Stochastic lorenz equations
Additional Information:Special issue: 10th ICFD Conference Series on Numerical Methods for Fluid Dynamics (ICFD 2010)
Publisher:Elsevier

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