Data association problems posed as multidimensional assignment problems: algorithm development

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Abstract
The central problem in multitarget-multisensor tracking is the data association problem of partitioning the observations into tracks and false alarms so that an accurate estimate of the true tracks can be recovered. Many previous and current methodologies are based on single scan processing, which is real-time, but often leads to a large number of partial and incorrect assignments, and thus incorrect track identification. The fundamental difficulty is that data association decisions once made are irrevocable. Deferred logic methods such as multiple hypothesis tracking allow correction of these misassociations and are thus considered to be the method for tracking a large number of targets. The corresponding data association problems are however NP-hard and must be solved in real-time. The current work develops a class of algorithms that produce near-optimal solutions in real-time and are potentially orders of magnitude faster than existing methods.

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