THE MULTI-SECRETARY PROBLEM WITH RANDOM LIFETIME: APPLICATIONS IN UAV TARGET SELECTION
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Authors
Moore, Alexander G.
Subjects
online optimization
optimization
unmanned systems
dynamic programming
optimization
unmanned systems
dynamic programming
Advisors
Szechtman, Roberto
Chen, Louis
Date of Issue
2024-06
Date
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Monterey, CA; Naval Postgraduate School
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Abstract
Unmanned systems have proven to be a flexible and cost-effective component of modern warfare. As the complexity and breadth of unmanned system operations expand, development of reliable decision support algorithms that can be scaled to numerous platforms will become increasingly important. We examine the problem of an unmanned aerial vehicle (UAV) that encounters targets at a regular rate. Each target carries a value according to a known probability distribution. The UAV operator must determine how best to allocate a finite store of munitions to maximize the expected value of targets engaged. Critically, UAV flight time is random, determined both by the UAV’s range and its likelihood of being killed or disabled by enemy actions. We develop an algorithm via dynamic programming that selects targets in an online fashion that maximizes the expected value of targets killed. A closed form version of the algorithm is developed for the base case in which UAV lifetime follows a Geometric distribution and target values follow a continuous Uniform distribution. Results are then broadened to a generic probability distribution and extended to the case in which UAV lifetime has an upper bound determined by battery or fuel capacity. Performance of the algorithm is assessed across several target value probability distributions and compared to the performance of two different “approximate DP” algorithms that do not have the same computational requirements of a traditional DP approach.
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Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.