Optimal Control of Stochastic Systems
Optimal Control of Stochastic Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
New sequential Monte Carlo methods for nonlinear dynamic systems
Statistics and Computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
Statistics and Computing
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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.