New multidimensional data association algorithm for multisensor-multitarget tracking
Aubrey B. Poore, Alexander James Robertson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Large classes of data association problems in multiple hypothesis tracking applications involving multiple and single sensor systems can be formulated as multidimensional assignment problems. Lagrangian relaxation methods have been shown to solve these problems to the noise level in the problem in real-time, especially for dense scenarios and for multiple scans of data from multiple sensors. This work presents a new class of algorithms that circumvent some of the shortcomings of previous algorithms. The computational complexity of the new algorithms is shown via some numerical examples to be linear in the number of arcs. Numerical results demonstrate the superior solution quality of the relaxation algorithm compared to proven greedy methods. Decomposition is also shown to provide improved execution times for clustered association problems that regularly arise in tracking.