Track fusion is one of the algorithm architectures for tracking multiple targets with data from multiple sensors. In track fusion for example, sensor-level tracks can be combined to form global-level tracks that are based on data from all the sensors. These multiple sensor, global- level tracks can then be fed back to the sensor-level trackers to reduce the data association errors. The global tracks, however, are cross-correlated with the sensor-level tracks. A method is needed to take this track-to-track cross-correlation into account. This cross- correlation of the global and sensor tracks must be considered when providing the global tracks to the sensor trackers as well as when providing the sensor tracks to the global tracker. Even without process noise, the global and sensor tracks are cross-correlated because they are based on common data. With feedback, both the global tracks and the tracks from each sensor are based on prior data from not only the sensor itself, but also the other sensors. This paper presents a method for dealing with the cross-correlations of the tracks in track fusion for feeding back the global level tracks. New methods have been recently developed for track fusion without process noise. These new methods address track fusion without feedback of global-level tracks to the lower levels. Application of these new methods are employed in this paper to deal with the complex cross-correlations involved when global tracks are fed back to the sensor-level trackers.