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Proceedings Paper

Use of joint data association probabilities for covariance consistency
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Paper Abstract

Covariance consistency is a critical element of a robust target tracking system. Target maneuvers and measurement origin uncertainty pose significant challenges to a tracking algorithm achieving covariance consistency. The Interacting Multiple Model (IMM) estimator is a nearly consistent estimator for tracking maneuvering targets. While the Probabilistic Data Association Filter (PDAF) achieves covariance consistency for a single target in presence of false alarms, achieving covariance consistency while tracking multiple closely-spaced targets is an open presence of false alarms, achieving covariance consistency while tracking multiple closely-spaced targets is an open issue. When using an unique assignment technique for associating measurements-to-track association probabilities are unity for each measurement-track pair. This processing of the measurements results in poor covariance consistency for closely-spaced targets. In this paper, the use of approximate association probabilities for each measurement-to-track pair is proposed for the unique assignments and included in the track filter processing of the measurement to enhance the covariance consistency for closely-spaced targets.

Paper Details

Date Published: 26 November 2001
PDF: 5 pages
Proc. SPIE 4473, Signal and Data Processing of Small Targets 2001, (26 November 2001); doi: 10.1117/12.492743
Show Author Affiliations
W. Dale Blair, Georgia Tech Research Institute (United States)
George C. Brown, Georgia Tech Research Institute (United States)


Published in SPIE Proceedings Vol. 4473:
Signal and Data Processing of Small Targets 2001
Oliver E. Drummond, Editor(s)

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