Many real world problems have a common underlying structure in which the task is to
estimate a latent dynamic state from measurements that are taken at specific time instances …

This paper presents a novel prognostic method that allows a proper characterization of the
uncertainty associated with the evolution in time of nonlinear dynamical systems. The …

An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart
Sampling Kalman Filter (S 2 KF) is introduced. It is based on a new low-discrepancy Dirac …

In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular
Nonlinear Kalman Filters, eg, the UKF, we do not rely on linearizing the measurement …

Monostatic and bistatic position and Doppler measurements used in radar and sonar
systems are nonlinear transformations of a Cartesian state. These nonlinearities pose a …

We consider estimating the hidden state of a discretetime stochastic nonlinear dynamic
system based on noisy measurements through Bayesian inference. This is an important …

A new Gaussian filter for estimating the state of nonlinear systems is derived that relies on
two main ingredients: i) the progressive inclusion of the measurement information and ii) a …

Bayesian Inverse Problems Page 1 Page 2 Bayesian Inverse Problems Fundamentals and
Engineering Applications Editors Juan Chiachío-Ruano University of Granada Spain Manuel …

A multivariate signal denoising method is proposed which employs a novel multivariate
goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete …

We introduce the unscented von Mises-Fisher filter (UvMFF), a nonlinear filtering algorithm
for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on …