Paper Info
Title
The Possibilistic Kalman Filter: Definition and Comparison With the Available Methods
Abstract
The Kalman filter (KF) is a commonly used algorithm for predicting the state variables of a system. It is based on the model of the system and some measurements (observed over time) that are characterized by their own uncertainty. This article defines a possibilistic KF whose main feature is to predict the values of the state variables and the associated uncertainty when uncertainty contributions of nonrandom nature are present. This possibilistic KF is defined in the mathematical framework of the possibility theory and employs random-fuzzy variables and the related mathematics since these variables can properly represent measurement results together with the associated uncertainty. A comparison with the available methods is provided, as well as the final validation.
Year
DOI
Venue
2021
10.1109/TIM.2020.3010193
IEEE Transactions on Instrumentation and Measurement
Keywords
DocType
Volume
Kalman filter (KF),measurement uncertainty,possibility distributions (PDs),random contributions,random-fuzzy variables (RFVs),systematic contributions
Journal
70
ISSN
Citations 
PageRank 
0018-9456
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
A. Ferrero137688.12
Harsha Vardhana Jetti200.34
Simona Salicone326333.62