The particle filtering algorithm was introduced in the 1990s as a numerical solution to the
Bayesian estimation problem for nonlinear and non-Gaussian systems and has been …

A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation in:
Monthly Weather Review Volume 146 Issue 1 (2018) Jump to Content Jump to Main Navigation …

We present a Rao-Blackwellized point mass filter (RB-PMF) as a deterministic counterpart of
the Rao-Blackwellized marginal particle filter (RB-MPF). The main advantage of the …

The authors consider the joint state and parameter estimation problem for dynamical models
where the system evolution is known and where the observations are linear with additive …

An adaptive covariance inflation scheme is proposed for the ensemble Kalman filter (EnKF)
to mitigate the loss of ensemble variance. Adaptive inflation methods are mostly based on a …

This paper presents an extension of the ensemble Kalman filter (EnKF) that can
simultaneously estimate the state vector and the observation error covariance matrix by …

In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential
algorithm analogous to our previously proposed approach (Ruggeri et al 2017 Bayesian …

A state space model consists of an unobservable Markov process for the evolution of the
state of a system together with an observation process given by noisy instantaneous …

The article presents studies on the estimation quality of a particle filter applied to small
multidimensional objects. For the purposes of the article, a new type of network has been …

In the paper a new grid (potentially linear, nonlinear and even semi-Markovian jump system)
was presented. All transition and measurement functions were proposed. Moreover, the …