Interpolation and Iteration for Nonlinear Filters

Abstract

We present a general form of the iteration and interpolation process used in implicit particle filters. Implicit filters are based on a pseudo-Gaussian representation of posterior densities, and are designed to focus the particle paths so as to reduce the number of particles needed in nonlinear data assimilation. Examples are given.

References in corpus (2)

• Sharp failure rates for the bootstrap particle filter in high dimensions
• Efficient Monte Carlo sampling by parallel marginalization

Cited by in corpus (3)

• Data Assimilation: A Mathematical Introduction
• Implicit particle filters for data assimilation
• Particle filters for high-dimensional geoscience applications: a review