Particle Filtering (PF) is used in prognostics applications by reason of its capability of
robustly predicting the future behavior of an equipment and, on this basis, its Residual …

Modeling physical systems often leads to discrete time state-space models with dependent
process and measurement noises. For linear Gaussian models, the Kalman filter handles …

Predictive Maintenance (PrM) exploits the estimation of the equipment Residual Useful Life
(RUL) to identify the optimal time for carrying out the next maintenance action. Particle …

We study the ergodic properties of a class of controlled stochastic differential equations
(SDEs) driven by a-stable processes which arise as the limiting equations of multiclass …

In this paper, we address univariate stochastic volatility models that allow for correlation of
the perturbations in the state and observation equations, ie, models with leverage. We …

Abstract Joint Cramér‐Rao lower bound (JCRLB) is very useful for the performance
evaluation of joint state and parameter estimation (JSPE) of non‐linear systems, in which the …

This letter introduces a Kalman Filter framework for systems with process noise and
measurements characterized by state-dependent, nonlinear conditional means and …

Particle Filtering (PF) is a model-driven approach widely used in prognostics, which requires
models of both the degradation process and the measurement acquisition system. In many …

The performance evaluation of state estimators for nonlinear regular systems, in which the
current measurement only depends on the current state directly, has been widely studied …

This paper provides theoretical results and numerical demonstration for nonlinear filtering of
systems with multiple timescales and correlated signal-sensor noise. The motivation of this …