Active Learning in Persistent Surveillance UAV Missions

The performance of many complex UAV decision-making problems can be extremely sensitive to small errors in the model parameters. One way of mitigating this sensitivity is by designing algorithms that more effectively learn the model throughout the course of a mission. This paper addresses this impor...

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Bibliographic Details
Main Authors: Redding, Joshua (Contributor), Bethke, Brett M. (Contributor), Bertuccelli, Luca F. (Contributor), How, Jonathan P. (Contributor)
Other Authors: Massachusetts Institute of Technology. Aerospace Controls Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
Format: Article
Language:English
Published: American Institute of Aeronautics and Astronautics, 2013-10-23T15:08:12Z.
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