An Improved Representation of Measurement Information Content Via the Distribution of the Kullback-Leibler Divergence

Abstract

Proper utilization of sensor networks is key in target-dense or measurement-scarce environments, such as in the creation and maintenance of reliable records for space objects in Earth orbit. In recent years, there have been many investigations of utilizing different information-theoretic measures as performance measures in allocating sensor tasks to maximize the information gained. More specifically, information divergences have been considered in sensor tasking schemes to effectively and efficiently utilize the available sensor resources. However, it is typical that only the expected information gain with respect to the measurement likelihood is considered, while the rest of the distribution of the divergence in question is disregarded. This work studies the full distribution of the Kullback-Leibler divergence and if the utilization of this knowledge when committing to an action regarding the acquisition of measurement information is beneficial.

Meeting Name

AAS/AIAA Astrodynamics Specialist Conference, 2017 (2017: Aug. 20-24, Stevenson, WA)

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Earth (planet); Information theory; Orbits; Sensor networks, Expected informations; Information divergence; Information theoretic measure; Kullback Leibler divergence; Measurement information; Performance measure; Sensor resources; Sensor tasking, Astrophysics

International Standard Book Number (ISBN)

978-087703645-6

International Standard Serial Number (ISSN)

0065-3438

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Univelt Inc., All rights reserved.

Publication Date

01 Aug 2018

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