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Distributed estimation based on information‐based covariance intersection algorithms
Author(s) -
Mahmoud Magdi S.
Publication year - 2016
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2638
Subject(s) - covariance intersection , kalman filter , covariance matrix , covariance , extended kalman filter , algorithm , intersection (aeronautics) , tracking (education) , particle filter , computer science , filter (signal processing) , ensemble kalman filter , information filtering system , fast kalman filter , control theory (sociology) , mathematics , estimation of covariance matrices , artificial intelligence , engineering , statistics , computer vision , machine learning , control (management) , psychology , pedagogy , aerospace engineering
Summary A distributed estimation approach is developed in this paper using information matrix filter on a distributed tracking system in which multiple sensors are tracking the same target. The information matrix filter version is derived from covariance intersection, weighted covariance and Kalman‐like particle filter, respectively. The steady performance of these filters is evaluated with different feedback strategies. The developed filters are then validated on an industrial utility boiler. Copyright © 2015 John Wiley & Sons, Ltd.