The safe fusion algorithm is benchmarked against three other methods in distributed target tracking scenarios. Safe fusion is a fairly unknown method similarly to, e.g., covariance intersection, that can be used to fuse potentially dependent estimates without double counting data. This makes it suitable for distributed target tracking, where dependencies are often unknown or difficult to derive. The results show that safe fusion is a very competitive alternative in five evaluated scenarios, while at the same time easy to implement and compute compared to the other evaluated methods. Hence, safe fusion is an attractive alternative in track to track fusion systems.