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Proceedings Paper

Track-to-track association and bias removal
Author(s): Lawrence D. Stone; Mark L. Williams; Thy M. Tran
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Paper Abstract

This paper develops methods for associating two sets of sensor tracks in the presence of missing tracks and translation bias. Key results include 1) extension of the maximum A Posteriori probability method of matching tracks to use feature information as well as kinematic information; 2) translation bias removal techniques that are computationally tractable for large numbers of tracks, and effective in the presence of missing tracks. These methods were evaluated by Monte Carlo simulation. The experimental results indicate that the maximum A Posteriori probability method with its adaptive threshold achieves close to its best performance for matching tracks without an additional threshold adjustment.

Paper Details

Date Published: 7 August 2002
PDF: 15 pages
Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); doi: 10.1117/12.478531
Show Author Affiliations
Lawrence D. Stone, Metron, Inc. (United States)
Mark L. Williams, Metron, Inc. (United States)
Thy M. Tran, Metron, Inc. (United States)


Published in SPIE Proceedings Vol. 4728:
Signal and Data Processing of Small Targets 2002
Oliver E. Drummond, Editor(s)

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