Physical systems typically evolve continuously whereas modern controllers and signal
processing devices invariably operate in discrete time. Hence sampling arises as a …

Due to its potential applications in robotics and navigation, recent years have witnessed
some progress in Kalman filter (KF) with norm constraints on the state. A noticeable …

Modern signal processing and control algorithms are invariably implemented digitally, yet
most real-world systems evolve in continuous time. Hence, the interaction between sampling …

Maximum likelihood estimation has a rich history. It has been successfully applied to many
problems including dynamical system identification. Different approaches have been …

ARX (AutoRegressive models with eXogenous variables) are the simplest models within the
equation error family but are endowed with many practical advantages concerning both their …

The last few decades have witnessed much development in filtering of systems with
Gaussian noises and arbitrary unknown inputs. Nonetheless, there are still some important …

Autocovariance least-squares (ALS) is a correlation-based noise covariance estimation
method that has received much attention recently. However, most existing works focus on …

This paper focuses on the problem of robust experiment design, ie, how to design an input
signal which gives relatively good estimation performance over a large number of systems …

Indirect methods for the identification of linear plant models on the basis of closed loop data
are based on the use of (reconstructed) input signals that are uncorrelated with the noise …

In this paper we obtain the maximum likelihood estimate of the parameters of discrete-time
linear models by using a dual time–frequency domain approach. We propose a formulation …