An Optimization Formulation of Information Theoretic Sensor Tasking
Abstract
The process of tracking and maintaining estimates for the numerous space objects in orbit about the Earth is a complex and highly demanding problem, as it requires robust state estimates in the presence of sparse data. As the number of space objects greatly outweigh the number of available sensors, a sensor tasking policy is necessary to properly utilize the available sensor resources. This work takes a common approach to the problem of sensor tasking, namely utilizing the first moment of the Kullback-Leibler divergence, and extends its use to consider entire measurement sets through the use of a reference time. The resulting method is applied in both the single-target and multitarget domains, and compared to more conventional approaches to illustrate its viability.
Recommended Citation
M. J. Gualdoni and K. J. DeMars, "An Optimization Formulation of Information Theoretic Sensor Tasking," Advances in the Astronautical Sciences, vol. 167, pp. 2481 - 2499, Springer, Jan 2018.
Department(s)
Mechanical and Aerospace Engineering
International Standard Book Number (ISBN)
978-087703657-9
International Standard Serial Number (ISSN)
0065-3438
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2018
Comments
U.S. Department of Education, Grant P200A150309