A data fusion algorithm for multisensor systems

The new data fusion algorithm presented in this paper allows one to combine information from different sensors in continuous time. Continuous-time decentralized Kalman filters (DKF) are used as data fusion devices on local subsystems. Such a structure gives the flexibility for reconfiguration of a c...

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Bibliographic Details
Published inFUSION 2002 : proceedings of the Fifth International Conference on Information Fusion : July 8-11, 2002, Loews Annapolis Hotel, Annapolis, Maryland, USA Vol. 1; pp. 341 - 345 vol.1
Main Author Vershinin, Y.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Bayesian methods
Control systems
Equations
Feedback
Multisensor systems
Noise measurement
Noise reduction
Sensor fusion
Sensor systems
State estimation
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ISBN9780972184410
0972184414
DOI10.1109/ICIF.2002.1021172

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Summary:The new data fusion algorithm presented in this paper allows one to combine information from different sensors in continuous time. Continuous-time decentralized Kalman filters (DKF) are used as data fusion devices on local subsystems. Such a structure gives the flexibility for reconfiguration of a control system. New subsystems can easily be added without needing any redesign of the whole system. The system does not require a central processor and therefore, in the case of failure of local subsystems (each of which includes a local processor, sensors and actuators) the overall system will continue to work. The simulation results show that the performance of the overall system degrades gracefully even if the sensors of subsystems fail or interconnections are broken. Furthermore, local Kalman filters can effectively reduce subsystem and measurement noise.
ISBN:9780972184410
0972184414
DOI:10.1109/ICIF.2002.1021172