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Proceedings Paper

Hidden Markov models for threat prediction fusion
Author(s): Kenneth N. Ross; Ronald D. Chaney
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Paper Abstract

This work addresses the often neglected, but important problem of Level 3 fusion or threat refinement. This paper describes algorithms for threat prediction and test results from a prototype threat prediction fusion engine. The threat prediction fusion engine selectively models important aspects of the battlespace state using probability-based methods and information obtained from lower level fusion engines. Our approach uses hidden Markov models of a hierarchical threat state to find the most likely Course of Action (CoA) for the opposing forces. Decision tress use features derived from the CoA probabilities and other information to estimate the level of threat presented by the opposing forces. This approach provides the user with several measures associated with the level of threat, including: probability that the enemy is following a particular CoA, potential threat presented by the opposing forces, and likely time of the threat. The hierarchical approach used for modeling helps us efficiently represent the battlespace with a structure that permits scaling the models to larger scenarios without adding prohibitive computational costs or sacrificing model fidelity.

Paper Details

Date Published: 3 April 2000
PDF: 12 pages
Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); doi: 10.1117/12.381643
Show Author Affiliations
Kenneth N. Ross, Alphatech, Inc. (United States)
Ronald D. Chaney, Alphatech, Inc. (United States)

Published in SPIE Proceedings Vol. 4051:
Sensor Fusion: Architectures, Algorithms, and Applications IV
Belur V. Dasarathy, Editor(s)

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