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Proceedings Paper

Centralized fusion multisensor/multitarget tracker based on multidimensional assignments for data association
Author(s): S. Chaffee; Aubrey B. Poore; Nenad Rijavec; Richard R. Gassner; Vincent C. Vannicola
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Paper 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.

Paper Details

Date Published: 5 July 1995
PDF: 12 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213009
Show Author Affiliations
S. Chaffee, Colorado State Univ. (United States)
Aubrey B. Poore, Colorado State Univ. (United States)
Nenad Rijavec, Colorado State Univ. (United States)
Richard R. Gassner, Air Force Rome Lab. (United States)
Vincent C. Vannicola, Air Force Rome Lab. (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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