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Optimal linear data fusion for systems with missing measurements

Mohamed, Shady M.K. and Nahavandi, Saeid 2009, Optimal linear data fusion for systems with missing measurements, in ICONS 2009 : Proceedings of The 2nd IFAC International Conference on Intelligent Control and Signal Processing, International Federation of Automatic Control, Laxenburg, Austria, pp. 1-4.

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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Title Optimal linear data fusion for systems with missing measurements
Author(s) Mohamed, Shady M.K.
Nahavandi, Saeid
Conference name IFAC International Conference on Intelligent Control Systems and Signal Processing (2nd : 2009 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 21-23 September 2009
Title of proceedings ICONS 2009 : Proceedings of The 2nd IFAC International Conference on Intelligent Control and Signal Processing
Editor(s) [Unknown]
Publication date 2009
Conference series Intelligent Control and Signal Processing Conference
Start page 1
End page 4
Publisher International Federation of Automatic Control
Place of publication Laxenburg, Austria
Keyword(s) data fusion
kalman filter
generalised inverse.
Summary In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.
Language eng
Field of Research 090699 Electrical and Electronic Engineering not elsewhere classified
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2009
Persistent URL http://hdl.handle.net/10536/DRO/DU:30025589
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Created: Thu, 25 Mar 2010, 13:06:04 EST by Shady Mohamed