Data association is a fundamental problem in multitarget-multisensor tracking. It entails selecting the most probable association between sensor measurements and target tracks from a very large set of possibilities. A solution is infeasible by direct computation even in modestly-sized applications. We describe an iterative method for solving the optimal data association problem in a distributed fashion; the work exploits the framework of graphical models, a powerful tool for encoding the statistical dependencies of a set of random variables. Our basic idea is to treat the measurement assignment for each sensor as a random variable, which is in turn represented as a node in an underlying graph. Neighboring nodes are coupled by the targets visible to both sensors. Thus we transform the data association problem to that of computing the maximum a posteriori (MAP) configuration in a graphical model to which efficient techniques can be applied. We use a tree-reweighted version of the usual max-product algorithm that either outputs the MAP data association, or acknowledges failure. For acyclic graphs, this message-passing algorithm can solve the data association problem directly and recursively with complexity O((n )2N) for N sensors and n targets. On graphs with cycles, the algorithm may require more iterations to converge, and need not output an unambiguous assignment. However, for the data association problems we consider, the coupling matrices involved in computations are inherently of low rank, and experiments show that the algorithm converges very quickly and finds the MAP configurations.
Multitarget-multisensor data association using the tree-reweighted max-product algorithm
2003
12 Seiten, 19 Quellen
Aufsatz (Konferenz)
Englisch
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