Paper Info
Title
The Kalman Filter Uncertainty Concept in the Possibility Domain
Abstract
The Kalman filter (KF) is one of the most important and common optimal recursive data processing algorithms in many applications characterized by linear dynamical behavior and affected by random zero-mean white Gaussian noise. However, when measurement processes are considered, inaccuracy is not only due to noise but also to several contributions to uncertainty that can be due to both random and uncompensated systematic effects. Therefore, when the KF is used on experimental data, all uncertainty contributions should be considered. While several proposals are available in the literature to modify the KF in order to consider different probability distributions and systematic effects, represented both in probability and possibility domains, they are not fully compliant with the uncertainty concept adopted in metrology. The aim of this paper is, hence, to reformulate the KF theory within the possibility domain in compliance with the measurement uncertainty concept, in order to be able to consider both the random and systematic contributions to uncertainty (regardless of their distribution) that may affect the measurement process. An experimental setup is considered and the results obtained under different assumptions are reported.
Year
DOI
Venue
2019
10.1109/tim.2018.2890317
IEEE Transactions on Instrumentation and Measurement
Keywords
Field
DocType
Uncertainty,Measurement uncertainty,Systematics,Noise measurement,Kalman filters,Probability density function,Covariance matrices
Data processing,Noise measurement,Metrology,Measurement uncertainty,Algorithm,Control engineering,Kalman filter,Probability distribution,Additive white Gaussian noise,Probability density function,Mathematics
Journal
Volume
Issue
ISSN
68
11
0018-9456
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
A. Ferrero137688.12
R. Ferrero29638.67
Wei Jiang3122.23
Simona Salicone426333.62