Authors:
Jiří Ajgl
and
Miroslav Šimandl
Affiliation:
University of West Bohemia in Pilsen, Czech Republic
Keyword(s):
Dynamic systems, State estimation, Optimal estimation, Sensor fusion, Filtering problems.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Optimization Problems in Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The paper deals with fusion of state estimates of stochastic dynamic systems. The goal of the contribution is to present main approaches to the estimate fusion which were developed during the last four decades. The hierarchical and decentralised estimation are presented and main special cases are discussed. Namely the following approaches, the distributed Kalman filter, maximum likelihood, channel filters, and the information measure, are introduced. The approaches are illustrated in numerical examples.