Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion

Currently there exist two commonly used measurement fusion methods for Kalm an-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 min imum-mean-square-error criterion. This paper, based on an analysis of the f used state estimate covariances of the two measurement fusion methods, show s that the two measurement fusion methods are functionally equivalent if th e sensors used for data fusion, with different and independent noise charac teristics, have identical measurement matrices. Also presented are simulati on results on state estimation using the two measurement fusion methods, fo llowed by the analysis of the computational advantages of each method.