Gaussian proposal density using moment matching in SMC methods |
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Authors: | S Saha P K Mandal Y Boers H Driessen A Bagchi |
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Institution: | (1) Department of Applied Mathematics, University of Twente, 7500 AE Enschede, The Netherlands;(2) THALES Nederland BV, Haaksbergerstraat 49, 7554 PA Hengelo, The Netherlands |
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Abstract: | In this article we introduce a new Gaussian proposal distribution to be used in conjunction with the sequential Monte Carlo
(SMC) method for solving non-linear filtering problems. The proposal, in line with the recent trend, incorporates the current
observation. The introduced proposal is characterized by the exact moments obtained from the dynamical system. This is in
contrast with recent works where the moments are approximated either numerically or by linearizing the observation model.
We show further that the newly introduced proposal performs better than other similar proposal functions which also incorporate
both state and observations.
This work was supported by a research grant from THALES Nederland BV. |
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Keywords: | Bayesian filtering Nonlinear dynamic system Sequential Monte Carlo methods Particle filtering Importance sampling Moment matching |
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