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.
Approximate evaluation of marginal association probabilities with belief propagation
IEEE Transactions on Aerospace and Electronic Systems ; 50 , 4 ; 2942-2959
2014-10-01
1837694 byte
Article (Journal)
Electronic Resource
English
Nonparametric belief propagation
IEEE | 2003
|Nonparametric Belief Propagation
British Library Conference Proceedings | 2003
|A Comparison of JPDA and Belief Propagation for Data Association in SSA
British Library Conference Proceedings | 2014
|Marginal cost congestion pricing under approximate equilibrium conditions
DSpace@MIT | 1998
|