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Accession Number ADA564803
Title Data Association by Loopy Belief Propagation.
Publication Date Jul 2010
Media Count 10p
Personal Author J. L. Williams R. A. Lau
Abstract Data association, or determining correspondence between targets and measurements, is a very difficult problem that is of great practical importance. In this paper we formulate the classical multi-target data association problem as a graphical model and demonstrate the remarkable performance that approximate inference methods, specifically loopy belief propagation can provide. We apply it to calculating marginal association weights (e.g., for JPDA) for single scan and multiple scan problems, and to calculating a MAP hypothesis (i.e., multi-dimensional assignment). Through computational experiments involving challenging problems we demonstrate the remarkable performance of this very simple, polynomial time algorithm; e.g., errors of less than 0.026 in marginal association weights and finding the optimal 5D assignment 99.4% of the time for a problem with realistic parameters. Impressively the formulation commits smaller errors in association weights in challenging environments, i.e., in problems with low Pd and/or high false alarm rates. Our formulation paves the way for the expanding literature on approximate inference methods in graphical models to be applied to classical data association problems.
Keywords Algorithms
Data association
Foreign reports
Graphical models
Loopy belief propagation
Multi-dimensional assignment

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
Corporate Author Defence Science and Technology Organisation, Edinburgh, (Australia) Intelligence Surveillance and Reconnaissance Division.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1303
Contract Number N/A

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