Currently there exist two commonly used measurement fusion methods for Kalman-filter-based multisensor data fusion. The first (Method I) simply merges the multisensor data through the observation vector of the Kalman filter, whereas the second (Method II) combines the multisensor data based on a minimum-mean-square-error criterion. This paper, based on an analysis of the fused state estimate covariances of the two measurement fusion methods, shows that the two measurement fusion methods are functionally equivalent if the sensors used for data fusion, with different and independent noise characteristics, have identical measurement matrices. Also presented are simulation results on state estimation using the two measurement fusion methods, followed by the analysis of the computational advantages of each method.
Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion
IEEE Transactions on Aerospace and Electronic Systems ; 37 , 1 ; 273-279
2001-01-01
219556 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter
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