ABSTRACT
Performing data fusion requires estimates of the state of a system to be converted to a common representation. The mean and covariance representation is the lingua franca of modern systems engineering. In particular, the covariance intersection (CI) 2 and Kalman lter (KF)3 algorithms provide mechanisms for fusing state estimates de ned in terms of means and covariances, where each mean vector de nes the nominal state of the system and its associated error covariance matrix de nes a lower bound on the squared error. However, most data fusion applications require the fusion of mean and covariance estimates de ning the state of a system in different coordinate frames. For example, a tracking system might maintain estimates in a global Cartesian coordinate frame, whereas observations of the tracked objects are generated in the local coordinate frames of various sensors. Therefore, a transformation must be applied to convert between the global coordinate frame and each local coordinate frame.
