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 are combined to form global-level tracks that are based on data from all the sensors. These multiple sensor, global-level can then be fed back to the sensor-level trackers to reduce the data association errors and to improve the accuracy of the sensor-level tracks. The global tracks, however, are cross-correlated with the sensor-level tracks. This track- to-track cross-correlation should be taken into account in algorithm design. This cross-correlation must be considered when providing the global tracks to the sensor trackers as well as when providing the sensor tracks to the global tracker. 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 goes beyond a previous paper and presents algorithm architectures are compared qualitatively.