Use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking, and situation assessment

Leland Stewart; Perry McCarty Jr.

doi:10.1117/12.138224

9 July 1992 Use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking, and situation assessment

Leland Stewart, Perry McCarty Jr.

Author Affiliations +

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138224
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Leland Stewart and Perry McCarty Jr. "Use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking, and situation assessment", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138224

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available