Efficient particle filtering through residual nudging

Abstract

We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging is to monitor, and if necessary, adjust the residual norm of a state estimate in the observation space so that it does not exceed a pre-specified threshold. We suggest a rule to choose the pre-specified threshold, and construct a state estimate accordingly to achieve this objective. Numerical experiments suggest that introducing residual nudging to a particle filter may (substantially) improve its performance, in terms of filter accuracy and/or stability against divergence, especially when the particle filter is implemented with a relatively small number of particles.

Accepted to publish in Quarterly Journal of the Royal Meteorological Society (QJRMS)

References in corpus (5)

• Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems
• Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters
• Scaled unscented transform Gaussian sum filter: theory and application
• Ensemble Kalman filtering with residual nudging
• Reply to "Comment on 'Ensemble Kalman filter with the unscented transform'"

Cited by in corpus (3)

• A local ensemble transform Kalman particle filter for convective scale data assimilation
• Ensemble Kalman filtering with a divided state-space strategy for coupled data assimilation problems
• Ensemble Kalman filtering with residual nudging: an extension to state estimation problems with nonlinear observation operators