5 July 1995 Centralized fusion multisensor/multitarget tracker based on multidimensional assignments for data association
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Abstract
Large classes of data association problems in multiple hypothesis tracking applications, including sensor fusion, 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 the difficulties of similar previous algorithms. The computational complexity of the new algorithms is shown via some numerical examples to be linear in the number of arcs.
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S. Chaffee, S. Chaffee, Aubrey B. Poore, Aubrey B. Poore, Nenad Rijavec, Nenad Rijavec, Richard R. Gassner, Richard R. Gassner, Vincent C. Vannicola, Vincent C. Vannicola, "Centralized fusion multisensor/multitarget tracker based on multidimensional assignments for data association", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213009; https://doi.org/10.1117/12.213009
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