Utilizing Information Statistics in Multi-Observation Sensor Tasking


Effective sensor tasking is key in target-dense or measurment-sparse environments, such as in the creation and maintenance of reliable records for space objects in earth orbit. This problem is well studied, and a variety of solutions have been developed, many based in the area of information theory. These approaches utilize information divergence measures in the decision making process; these measures are used as a means of quantifying the strength of an update, thus giving the sensor tasking scheme an objective to optimize. While recent work has delved further into these divergences by determining their higher order moments, a formulation for handling multiple observations has yet to be addressed directly. This work proposes a methodology for using information divergences, specifically the Kullback-Leibler divergence, in deciding upon a set of observations to task for subsequent collection and processing.

Meeting Name

Space Flight Mechanics Meeting, 2018 (2018: Jan. 8-12, Kissimmee, FL)


Mechanical and Aerospace Engineering

Keywords and Phrases

Decision making; Information theory; Mammography; Mechanics; Orbits; Space flight, Decision making process; Higher order moments; Information divergence; Information statistics; Kullback Leibler divergence; Sensor tasking; Space objects; Sparse environments, Earth (planet)

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2018 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

01 Jan 2018