  This paper presents the generalized optimal sub-pattern assignment (GOSPA)
metric on the space of finite sets of targets. Compared to the well-established
optimal sub-pattern assignment (OSPA) metric, GOSPA is unnormalized as a
function of the cardinality and it penalizes cardinality errors differently,
which enables us to express it as an optimisation over assignments instead of
permutations. An important consequence of this is that GOSPA allows us to
penalize localization errors for detected targets and the errors due to missed
and false targets, as indicated by traditional multiple target tracking (MTT)
performance measures, in a sound manner. In addition, we extend the GOSPA
metric to the space of random finite sets, which is important to evaluate MTT
algorithms via simulations in a rigorous way.
