DocumentCode :
3184465
Title :
A covariance analysis technique for the federated Kalman filter
Author :
Felter, Stephen C.
Author_Institution :
IBM Federal Sector Div., Owego, NY, USA
fYear :
1992
fDate :
18-22 May 1992
Firstpage :
399
Abstract :
A covariance analysis technique for the federated Kalman filter based on suboptimal system models is described. A review of the federated filter is given. Performance techniques for suboptimal filters are reviewed and extended to apply to the federated filter. A simple example is provided to demonstrate the technique. The results are compared with those from a Monte Carlo simulation for validation. The covariance analysis technique is shown to provide equivalent results, and requires substantially less computer time for execution. The covariance analysis solution was obtained in much less time that the Monte Carlo results, allowing for the Kalman filter designer to spend more time evaluating results and less time waiting for simulations to be completed
Keywords :
Kalman filters; computational complexity; filtering and prediction theory; Monte Carlo simulation; computer time; covariance analysis; federated Kalman filter; suboptimal filters; Covariance matrix; Differential equations; Fault tolerance; Filtering theory; Information filtering; Information filters; Sensor phenomena and characterization; Sensor systems; State estimation; State feedback;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Aerospace and Electronics Conference, 1992. NAECON 1992., Proceedings of the IEEE 1992 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-0652-X
Type :
conf
DOI :
10.1109/NAECON.1992.220540
Filename :
220540
Link To Document :
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