The Mean Field Ensemble Kalman Filter: Near-Gaussian Setting

TL;DR

This paper provides the first theoretical analysis of the ensemble Kalman filter's accuracy beyond Gaussian assumptions, demonstrating stability and small error in near-Gaussian settings using a novel metric.

Contribution

It introduces a stability framework and error bounds for the mean field ensemble Kalman filter in non-Gaussian contexts, extending its theoretical understanding.

Findings

The ensemble Kalman filter remains accurate near Gaussian problems.

Stability estimates control the filter's error in terms of distribution differences.

Results are generalized to the Gaussian projected filter, related to the unscented Kalman filter.

Abstract

The ensemble Kalman filter is widely used in applications because, for high dimensional filtering problems, it has a robustness that is not shared for example by the particle filter; in particular it does not suffer from weight collapse. However, there is no theory which quantifies its accuracy as an approximation of the true filtering distribution, except in the Gaussian setting. To address this issue we provide the first analysis of the accuracy of the ensemble Kalman filter beyond the Gaussian setting. We prove two types of results: the first type comprise a stability estimate controlling the error made by the ensemble Kalman filter in terms of the difference between the true filtering distribution and a nearby Gaussian; and the second type use this stability result to show that, in a neighbourhood of Gaussian problems, the ensemble Kalman filter makes a small error, in comparison with the true filtering distribution. Our analysis is developed for the mean field ensemble Kalman filter. We rewrite the update equations for this filter, and for the true filtering distribution, in terms of maps on probability measures. We introduce a weighted total variation metric to estimate the distance between the two filters and we prove various stability estimates for the maps defining the evolution of the two filters, in this metric. Using these stability estimates we prove results of the first and second types, in the weighted total variation metric. We also provide a generalization of these results to the Gaussian projected filter, which can be viewed as a mean field description of the unscented Kalman filter.