Target tracking sensors and algorithms are usually evaluated using Monte Carlo simulations covering a large
parameter space. We show a tracker for which the evaluation can be greatly simplified. We apply it to the one
dimensional crossing track problem (e.g. ground target tracking in a dense target environment, where targets are
confined to a road), and estimate the probability that measurements and tracks are incorrectly associated. If only
position is measured, we find the probability of a misassociation is a very simple analytic function of the relevant
parameters: measurement standard deviation, measurement interval, target density, and target acceleration. For
normally distributed target velocities, the average time between misassociations also has a simple form. We
suggest roll-up metrics for tracking sensors and tracking problems.