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Proceedings Paper

Architectures, algorithms, and applications using Bayesian networks
Author(s): Todd Kingsbury
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Paper Abstract

A Bayesian network is a tree structure where each branch represents a classification candidate. The leaves of the tree represent observable target features such as frequency or length. An optimized tree groups similar features together, e.g. frequency and pulse width, while collecting dissimilar or disparate information, e.g. spectral and kinematics, all within the same unifying structure. A vehicular track then is a subset of the a priori candidate library and contains only feasible branches. The algorithm for updating the confidence of each feasible candidate according to Bayes' rule is embedded in each track, as is the ability of a track to learn, apply a priori probability distributions, switch modes, switch among kinematics models, apply tracking history to classification and apply classification history to tracking, and support multisensor correlation and sensor fusion.

Paper Details

Date Published: 6 June 2011
PDF: 10 pages
Proc. SPIE 8064, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011, 806407 (6 June 2011); doi: 10.1117/12.882173
Show Author Affiliations
Todd Kingsbury, General Dynamics Advanced Information Systems (United States)


Published in SPIE Proceedings Vol. 8064:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011
Jerome J. Braun, Editor(s)

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