Karlsruhe Institute of Technology
The distributed processing of measurements and the subsequent data fusion is called Track-to-Track fusion. Although a solution for the Track-to-Track fusion that is equivalent to a central processing scheme has been proposed, this algorithm suffers from strict requirements regarding the local availability of knowledge about utilized models of the remote nodes. By means of simple examples, the authors investigate the effects of incorrectly assumed models and trace the errors back to a bias, which they derive in closed form. They propose an extension to the exact Track-to-Track fusion algorithm that corrects the bias after arbitrarily many time steps.