In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for
linear Gaussian state-space models with inaccurate process and measurement noise …

Fourier transform infrared (FTIR) imaging spectrometers are often corrupted by the problems
of band overlap and random noise during the infrared spectrum acquisition process. Such …

We consider a family of divergence-based minimax approaches to perform robust filtering.
The mismodeling budget, or tolerance, is specified at each time increment of the model …

Rolling element bearings (REBs) play an essential role in modern machinery and their
condition monitoring is significant in predictive maintenance. Due to the harsh operating …

We investigate the problem of constructing exponentially converging estimates of the state of
a continuous-time system from state measurements transmitted via a limited-data-rate …

Chatter is a kind of self-excited vibration which will destroy the manufacturing process badly.
The detection or identification of chatter is attracting considerable interest for several years …

Modeling and identification of high-dimensional stochastic processes is ubiquitous in many
fields. In particular, there is a growing interest in modeling stochastic processes with simple …

This paper deals with the robust estimation problem of a signal given noisy observations.
We assume that the actual statistics of the signal and observations belong to a ball about the …

This paper presents a semi-parametric algorithm for online learning of a robot inverse
dynamics model. It combines the strength of the parametric and non-parametric modeling …

Kernel-based methods have been recently introduced for linear system identification as an
alternative to parametric prediction error methods. Adopting the Bayesian perspective, the …