Share Email Print

Proceedings Paper

Use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking, and situation assessment
Author(s): Leland Stewart; Perry McCarty
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper describes the use of Bayesian belief networks for the fusion of continuous and discrete information. Bayesian belief networks provide a convenient and straightforward way of modeling the relationships between uncertain quantities. They also provide efficient computational algorithms. Most current applications of belief networks are restricted to either discrete or continuous quantities. We present a methodology that allows both discrete and continuous variables in the same network. This extension makes possible the fusion of information from, or inferences about, such diverse quantities as sensor output, target location, target type or ID, intent, operator judgment, behavior profile, etc.

Paper Details

Date Published: 9 July 1992
PDF: 9 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138224
Show Author Affiliations
Leland Stewart, Lockheed Palo Alto Research Lab. (United States)
Perry McCarty, Lockheed Palo Alto Research Lab. (United States)

Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top