Filtering and smoothing algorithms are key tools to develop decision-making strategies and
parameter identification techniques in different areas of research, such as economics …

For the problem of multi-mode state estimation in actual train operation, this paper proposes
a nonlinear non-gaussian high-precision parallel Kalman filter group (NN-HEKFG) …

The problem of state estimation of a linear, dynamical state-space system where the output
is subject to quantization is challenging and important in different areas of research, such as …

In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-
in-Variables systems is developed. We consider that the noise-free input signal is Gaussian …

In this paper, we address the problem of system identification for Wiener state–space
models. Our approach is based on the Maximum Likelihood method and the Expectation …

In this paper, we develop a novel filtering algorithm for Hammerstein-Wiener State-Space
Systems. The likelihood function of the noisy nonlinear output signal given the system state …

This paper concerns the identification of continuous-time systems in state-space form that
are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue …

In this paper, we develop a novel Bayesian smoothing method for obtaining the smoothed
probability density functions of Hammerstein-Wiener state-space systems and the …

In this paper, we develop a filtering algorithm for Wiener systems written in state-space form
which considers correlated noise sources. The output non-linearity is approximated by using …

This paper focuses on the state estimation of an Anaerobic Digestion (AD) model using two
filtering algorithms: the Extended Kalman Filter (EKF) and the Particle Filter (PF). AD is a …