Optimal multisensor data fusion for linear systems with missing measurements

Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility...

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Bibliographic Details
Published in2008 IEEE International Conference on System of Systems Engineering pp. 1 - 4
Main Authors Korany Mohamed, Shady M., Nahavandi, Saeid
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Covariance matrix
Estimation error
Filters
Intelligent systems
Linear systems
Loss measurement
Noise measurement
Sensor fusion
Target tracking
Working environment noise
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Summary:Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Illustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.
ISBN:9781424421725
1424421721
DOI:10.1109/SYSOSE.2008.4724205