Multi-entity Bayesian network for the handling of uncertainties in SATCOM links

Xin Tian; Genshe Chen; K. C. Chang; Todd Martin; Tien Nguyen; Khanh Pham; Erik Blasch

doi:10.1117/12.2190854

22 May 2015 Multi-entity Bayesian network for the handling of uncertainties in SATCOM links

Xin Tian, Genshe Chen, K. C. Chang, Todd Martin, Tien Nguyen, Khanh Pham, Erik Blasch

Proceedings Volume 9469, Sensors and Systems for Space Applications VIII; 94690B (2015) https://doi.org/10.1117/12.2190854
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Accurate prediction of satellite communications (SATCOM) data link loss is critical for SATCOM systems to effectively achieve required Quality of Service (QoS) and link availability. A major challenge is to account for various sources of uncertainties (such as atmospheric loss, rain loss, depolarization loss, pointing offset loss, etc.,) and their impacts on the aggregated link loss. This paper investigates the use of Bayesian Network (BN) for acquiring accurate SATCOM link loss estimation and link budget analysis over various modulation and coding schemes. Based on the proposed BN models, a SATCOM Bayesian Network Analysis toolbox has been developed to support link budget analysis and decision making for robust SATCOM applications.

Citation Download Citation

Xin Tian, Genshe Chen, K. C. Chang, Todd Martin, Tien Nguyen, Khanh Pham, and Erik Blasch "Multi-entity Bayesian network for the handling of uncertainties in SATCOM links", Proc. SPIE 9469, Sensors and Systems for Space Applications VIII, 94690B (22 May 2015); https://doi.org/10.1117/12.2190854

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