  Data association, the problem of reasoning over correspondence between
targets and measurements, is a fundamental problem in tracking. This paper
presents a graphical model formulation of data association and applies an
approximate inference method, belief propagation (BP), to obtain estimates of
marginal association probabilities. We prove that BP is guaranteed to converge,
and bound the number of iterations necessary. Experiments reveal a favourable
comparison to prior methods in terms of accuracy and computational complexity.
