Abstract
The ever-increasing demand in surveillance is to produce highly accurate target and track identification and estimation in real-time, even for dense target scenarios and in regions of high track contention. The use of multiple sensors, through more varied information, has the potential to greatly enhance target identification and state estimation. For multitarget tracking, the processing of multiple scans all at once yields high track identification. However, to achieve this accurate state estimation and track identification, one must solve an NP-hard data association problem of partitioning observations into tracks and false alarms in real-time. Over the last ten years a new approach to the problem formulation based on multidimensional assignment problems and near optimal solution in real-time by Lagrangian relaxation has evolved and is proving to be superior to all other approaches. This work reviews the problem formulation and algorithms with some suggested future directions.
This work was partially supported by the Air Force Office of Scientific Research through AFOSR Grant Number F49620–97–1–0273 and by the Office of Naval Research through ONR Grant Number N00014–99–1–0118.
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Poore, A.B. (2000). Multidimensional Assignment Problems Arising in Multitarget and Multisensor Tracking. In: Pardalos, P.M., Pitsoulis, L.S. (eds) Nonlinear Assignment Problems. Combinatorial Optimization, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3155-2_2
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