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Abstract : |
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. With N sensors and n targets in the detection range of each sensor, even with perfect detection there are (n!) N different configurations which renders infeasible a solution 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, which are a powerful tool for encoding the statistical dependencies of a set of random variables and are widely used in many applications (e.g., computer vision, error-correcting codes). 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 (e.g., the max-product/min-sum algorithm) 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!) 2 N ?. On graphs with cycles, the algorithm, |
