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Proceedings Paper

New multidimensional data association algorithm for multisensor-multitarget tracking
Author(s): Aubrey B. Poore; Alexander J. Robertson
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Paper Abstract

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.

Paper Details

Date Published: 1 September 1995
PDF: 12 pages
Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217718
Show Author Affiliations
Aubrey B. Poore, Colorado State Univ. (United States)
Alexander J. Robertson, BDM Federal, Inc. (United States)


Published in SPIE Proceedings Vol. 2561:
Signal and Data Processing of Small Targets 1995
Oliver E. Drummond, Editor(s)

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