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Proceedings Paper

K-near optimal solutions to improve data association in multiframe processing
Author(s): Aubrey B. Poore; Xin Yan
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Paper Abstract

The problem of data association remains central in multitarget, multisensor, and multiplatform tracking. Lagrangian relaxation methods have been shown to yield near optimal answers in real-time. The necessity of improvement in the quality of these solutions warrants a continuing interest in these methods. A partial branch-and-bound technique along with adequate branching and ordering rules are developed. Lagrangian relaxation is used as a branching method and as a method to calculate the lower bound for subproblems. The result shows that the branch-and-bound framework greatly improves the solutions in less time than relaxation alone.

Paper Details

Date Published: 4 October 1999
PDF: 9 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364041
Show Author Affiliations
Aubrey B. Poore, Colorado State Univ. (United States)
Xin Yan, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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