New class of Lagrangian-relaxation-based algorithms for fast data association in multiple hypothesis tracking applications
Aubrey B. Poore, Alexander James Robertson, Peter J. Shea · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Large classes of data association problems in multiple hypothesis tracking applications, including sensorfusion, can be formulated as multidimensional assignment problems. Lagrangian relaxation methods have beenshown to solve these problems to the noise level in the problem in real-time, especially for dense scenarios andfor multiple scans of data from multiple sensors. This work presents a new class of algorithms that circumventthe difficulties of similar previous algorithms. The computational complexity of the new algorithms is shownvia some numerical examples to be linear in the number of arcs.