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Proceedings Paper

New class of Lagrangian relaxation-based algorithms for fast data association in multiple hypothesis tracking applications
Author(s): Aubrey B. Poore; Alexander J. Robertson; Peter J. Shea
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Paper Abstract

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.

Paper Details

Date Published: 5 July 1995
PDF: 11 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213069
Show Author Affiliations
Aubrey B. Poore, Colorado State Univ. (United States)
Alexander J. Robertson, Colorado State Univ. (United States)
Peter J. Shea, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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