Track-to-track association and bias removal

Lawrence D. Stone; Mark L. Williams; Thy M. Tran

doi:10.1117/12.478531

7 August 2002 Track-to-track association and bias removal

Lawrence D. Stone, Mark L. Williams, Thy M. Tran

Proceedings Volume 4728, Signal and Data Processing of Small Targets 2002; (2002) https://doi.org/10.1117/12.478531
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Lawrence D. Stone, Mark L. Williams, and Thy M. Tran "Track-to-track association and bias removal", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); https://doi.org/10.1117/12.478531

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