The problem of associating data in a domain with noisy sensor inputs is of considerable importance in a wide variety
of problem areas. Data association algorithms provide an approach for automatically correlating and combining incoming
sensor data. A number of association algorithms have been developed; however, evaluating the effectiveness of these
algorithms is difficult because traditional evaluation methods fail to provide meaningful meansures of relative merit.
These traditional measures are troublesome because the type I and type II errors upon which they are based lose all meaning
after reports are combined in a data base. This paper describes a test bed which uses an alternative approach for measuring the
performance of association algorithms. Like the traditional measures, the approach described here requires the use of
simulated sensor data. The evaluation procedure is based on a measure of the distance between a baseline representation and
the representation produced by the association algorithm at some time instant. Two choices for this baseline representation
are listed and scores are defmed between these baselines and an algorithm's representation. A description of the test bed
architecture which implements this evaluation procedure is provided, as well as, sample outputs from performing algorithm
evaluations in the test bed.