Structural health monitoring (SHM) is becoming increasingly attractive for its potentialities in
many application contexts, such as civil and aeronautical engineering. In these scenarios …

This paper presents an overview of the main methods used to identify autoregressive
models with additive noises. The classification of identification methods is given. For each …

Abstract Structural Health Monitoring (SHM) methodologies are taking advantage of the
development of new families of MEMS sensors and of the available network technologies …

This paper presents four new methods for estimating the parameters of an autoregressive
(AR) process based on observations corrupted by white noise. The first three methods are …

From the ox carts and pottery wheels the spacecrafts and disk drives, efficiency and quality
has always been dependent on the engineer's ability to anticipate and control the effects of …

This paper considers the problem of determining linear relations from data affected by
additive noise in the context of the Frisch scheme. The loci of solutions of the Frisch scheme …

This paper proposes a new method for estimating the parameters of an autoregressive (AR)
signal from observations corrupted by white noise. The feature of the new method is that the …

Maximum likelihood autoregressive (AR) model parameter estimation problem with
independent snapshots observed under white Gaussian measurement noise is studied. In …

In this paper, we address the problem of modeling data with periodic autoregressive (PAR)
time series and additive noise. In most cases, the data are processed assuming a noise-free …

Abstract Jeffrey's divergence (JD), which is the symmetric version of the Kullback–Leibler
divergence, has been used in a wide range of applications, from change detection to clutter …