Global Optimality and Completely Functional Equivalence of Two Weighted Measurement Fusion Algorithms

For Kalman filtering-based multisensor data fusion, there are two weighted measurement fusion algorithms. Using the Kalman method, it is proved that compared with the centralized measurement fusion algorithm, they have the global optimality and completely functional equivalence. Not only they can give the globally optimal Kalman estimators (filter, predictor, and smoother) , white noise estimators, and signal estimators, but also the computational burden can be reduced obviously. They are adapted for real time applications.