EnKF within the Marginalized Particle Filter and Its Application for Adaptive Estimation of Inflation Factor

0346616 - ÚTIA 2011 US eng A - Abstract
Hofman, Radek - Šmídl, Václav
EnKF within the Marginalized Particle Filter and Its Application for Adaptive Estimation of Inflation Factor.
2010 The Meeting of the Americas. Americam Geophzsical Society, 2010. s. 1-2.
[2010 The Meeting of the Americas. 08.08.2010-12.08.2010, Foz do Iguaçu]
R&D Projects: GA MŠMT(CZ) 1M0572
Institutional research plan: CEZ:AV0Z10750506
Keywords : ensemble Kalman filter * inflation factor * adaptive tunning * observation error * Rao-Blackwellized particle filter
Subject RIV: BC - Control Systems Theory
http://library.utia.cas.cz/separaty/2010/AS/hofman-enkf within the marginalized particle filter and its application for adaptive estimation of inflation factor.pdf

We are concerned with Bayesian approach to data assimilation. Many common assimilation techniques, such as Kalman filtering and particle filtering, are special cases of the approximative solution of the Bayesian equations. Both of these techniques use a single approximation for the posterior probability density function (pdf) of the state vector. In this paper we advocate the use of marginalized particle filter (MPF, also known as Rao-Blackwelized particle filter), which splits the state vector in two disjoint parts and approximates their densities differently. Simulation study was performed as a twin experiment when measurements are sampled from a twin model and perturbed. The resulting assimilation scheme proved to be robust with respect to initial conditions and the inherent adaptivity of the inflation factor accelerated convergence to the simulated values.
Permanent Link: http://hdl.handle.net/11104/0187596