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Recently, it has become clear that determining a ranked set of assignments allows computation of very good approximations to the data association problem. Several algorithms have been proposed but only two return the k-best assignments in reasonable time. One is Danchick and Newnams'  algorithm, which is based on the recognition that determining the best assignment is a classical assignment problem and that determining a ranked set of assignments may be accomplished by solving a series of modified copies of the initial assignment problem. The other algorithm is originally due to Murty  and was most recently described within the context of multitarget tracking. We evaluate the two algorithm using randomly generated data and data obtained from an electrooptical sensor simulation in which 90 missiles are launched. These evaluations show that Murty's algorithm perform significantly better in all scenarios. We show the relationship between the two algorithms and how Danchick and Newnam's algorithm can be very easily modified to Murty's algorithm. Experimental results using Murty's algorithm suggest that a solution to the real-time data association problem is now feasible.