This paper presents an efficient method of multi-sensor estimation that can be used with asynchronous and synchronous sensors. A decentralized architecture is used for the fusion of information obtained from several asynchronous measurements. The issue of the synchronization of the information, which is critical in the proposed method, is addressed. The information form of the Kalman filter (information filter) is used as the main algorithm for estimation. The method is demonstrated with the implementation of a navigation system for an autonomous land vehicle. The integrity issue is also addressed with the implementation of multiple independent estimation loops. The proposed method allows for efficient fusion of information obtained from different measurements for covariance reduction, while providing the benefits of decentralized estimation architecture for integrity purposes. The resulting estimates are equivalent to an optimal centralized filter when the loops incorporate all the information available in the system. The information obtained from each measurement is then broadcast to the other loops after being synchronized. This information is used in an assimilation stage to achieve more accurate estimates. The assimilation frequency is also discussed considering the trade off of fault detectability and estimation covariance reduction. The performance of the navigation method is examined by comparing the resulting position estimates to those of independent navigation loops.
Decentralized architecture for asynchronous sensors
Autonomous Robots ; 6 , 2 ; 147-164
1999
18 Seiten, 17 Quellen
Article (Journal)
English
Advances in asynchronous and decentralized estimation
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