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Distributed fusion Kalman filtering under binary sensors
Author(s) -
Zhang Yuchen,
Chen Bo,
Yu Li
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4874
Subject(s) - covariance intersection , kalman filter , binary number , covariance , sensor fusion , computer science , fast kalman filter , fusion , intersection (aeronautics) , information fusion , extended kalman filter , algorithm , artificial intelligence , control theory (sociology) , mathematics , engineering , statistics , control (management) , linguistics , philosophy , arithmetic , aerospace engineering
Summary Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.