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Proceedings Paper

Sensor fusion cognition using belief filtering for tracking and identification
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Paper Abstract

Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks, To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a set of targets and an automatic target recognition algorithm uses the pose estimate to calculate an accumulated target-belief classification confidence measure. The human integrates the target track information and classification confidence measures to determine the number and identification of targets. This paper implements the cognitive belief filtering approach for sensor fusion and resolves target identity through a set-theory approach by determining a plausible set of targets being tracked.

Paper Details

Date Published: 12 March 1999
PDF: 10 pages
Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341347
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Lang Hong, Wright State Univ. (United States)

Published in SPIE Proceedings Vol. 3719:
Sensor Fusion: Architectures, Algorithms, and Applications III
Belur V. Dasarathy, Editor(s)

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