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Parallel Particle Filtering

Journal of Parallel and Distributed Computing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brun, Olivier, Teuliere, Vincent, Garcia, Jean-Marie   +2 more
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Particle Filters

2015
This chapter explains how the key difficulty that arises when the Bayesian estimation of DSGE models is extended from linear to nonlinear models is the evaluation of the likelihood function, and focuses on the use of particle filters to accomplish this task. The basic bootstrap particle filtering algorithm is remarkably straightforward, but may perform
Edward P. Herbst, Frank Schorfheide
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Particle Filter-Weight Estimation and Dual Particle Filter

2009 International Workshop on Intelligent Systems and Applications, 2009
When the clean state is not available, a dual estimation approach is required. A dual algorithm, dual particle filter, for nonlinear state and parameters estimation is presented. Dual filter is combined with particle filter for nonlinear situation. Two separate particle filters run con-currently: one for signal estimation which is called particle state
Pengpai Fan, Li-Fen Sui, Bing Wang, Wei Wang   +3 more
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Monte Carlo filter particle filter

2015 European Control Conference (ECC), 2015
We propose a new realization method of the sequential importance sampling (SIS) algorithm to derive a new particle filter. The new filter constructs the importance distribution by the Monte Carlo filter (MCF) using sub-particles, therefore, its non-Gaussianity nature can be adequately considered while the other type of particle filter such as unscented
Masaya Murata, Hidehisa Nagano, Kunio Kashino   +2 more
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Improving Regularised Particle Filters

2001
The optimal filter computes the posterior probability distribution of the state in a dynamical system, given noisy measurements, by iterative application of prediction steps according to the dynamics of the state, and correction steps taking the measurements into account.
Musso, Christian, Oudjane, Nadia, Le Gland, François   +2 more
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Particle flow for particle filtering

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
Particle flow algorithms have been developed as an alternative to particle filtering. In these algorithms, there is no importance sampling, and particles are migrated from the prior to the posterior via a "flow", described by differential equations. Aside from a few special cases, implementations involve multiple approximations, and their impact on the
Yunpeng Li, Lingling Zhao, Mark Coates
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Particle flow auxiliary particle filter

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
Particle flow filters have been recently developed as an alternative approach for nonlinear filtering. The particles approximating the prior are migrated using differential equations to be distributed according to the posterior. Computationally tractable exact solutions only exist for linear Gaussian models.
null Yunpeng Li, null Lingling Zhao, Mark Coates   +2 more
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Particle Filters for Magnetoencephalography

Archives of Computational Methods in Engineering, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust evolutionary particle filter

ISA Transactions, 2015
The particle filter (PF) has been widely applied for non-linear filtering owing to its ability to carry multiple hypotheses relaxing the linearity and Gaussian assumptions. However, PF is inconsistent over time due to the loss of particle diversity caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process ...
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Nonlinear filtering with particle filters

2014
Convective phenomena in the atmosphere, such as convective storms, are characterized by very fast, intermittent and seemingly stochastic processes. They are thus difficult to predict with Numerical Weather Prediction (NWP) models, and difficult to estimate with data assimilation methods that combine prediction and observations.
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