Clustering approach to the multitarget multisensor tracking problem
Nassib Nabaa, Robert H. Bishop · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
In a multitarget environment, tracking systems must include methods for associating measurements to targets. The complexity of that task is compounded when data from multiple sensors is available. This paper presents a clustering approach to the multitarget multisensor tracking problem. The measurement set is partitioned into equivalence classes (clusters) and the data association problem is redefined to be one of associating the cluster centers and the tracks, resulting in a significant reduction in the size of the association problem. Track termination and track initiation are part of system design, therefore allowing the designed system to be tested on elaborate multitarget tracking scenarios involving an unknown and changing number of real aircraft trajectories. Methods for evaluating the performance of the tracking system, as well as the clustering algorithms are introduced. An equivalence relation clustering algorithm is derived and compared by Monte-Carlo simulations to the subtractive clustering algorithm. The tracking system is shown to effectively track seven crossing aircraft trajectories of different duration, in the presence of clutter. Track maintenance is performed by extended Kalman filters.