Evidential Integration In A Multi-Sensor System With Dependency Information

Youssef A. Bazzi; Brian T. Mitchell

doi:10.1117/12.969976

1 March 1990 Evidential Integration In A Multi-Sensor System With Dependency Information

Youssef A. Bazzi, Brian T. Mitchell

Author Affiliations +

Proceedings Volume 1198, Sensor Fusion II: Human and Machine Strategies; (1990) https://doi.org/10.1117/12.969976
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

Several methods for combining uncertain evidences in a multi-sensor system have been developed in the literature. Most of these methods make routine assumptions on the type of relation between these evidences, that is the evidences are independent. This assumption leads to serious questions as to the validity of the conclusions reached by systems which employ such schemes. This paper presents a technique for reasoning with dependency information in expert systems that acquire probabilistic evidence. Three types of depndency relations have been considered. The proposed technique is suitable for computerization, and results in a more certain hypothesis value when applied to distributed sensor system and to terminal reliability in computer communication networks.

Citation Download Citation

Youssef A. Bazzi and Brian T. Mitchell "Evidential Integration In A Multi-Sensor System With Dependency Information", Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); https://doi.org/10.1117/12.969976

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