Optimal deployment of unmanned aerial vehicles for border surveillance

Approved for public release; distribution is unlimited === Border surveillance is an important concern for most nations wanting to detect and intercept intruders that are trying to trespass a border. These intruders can include terrorists, drug traffickers, smugglers, illegal immigrants, and others...

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Main Author: Sözen, Volkan
Other Authors: Craparo, Emily M.
Published: Monterey, California: Naval Postgraduate School 2014
Online Access:http://hdl.handle.net/10945/42729
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-427292014-11-27T16:19:56Z Optimal deployment of unmanned aerial vehicles for border surveillance Sözen, Volkan Craparo, Emily M. Lucas, Thomas W. Operations Research Approved for public release; distribution is unlimited Border surveillance is an important concern for most nations wanting to detect and intercept intruders that are trying to trespass a border. These intruders can include terrorists, drug traffickers, smugglers, illegal immigrants, and others who represent a threat to national interests. Unmanned aerial vehicles (UAVs) allow for modernization and improvement of border surveillance. There are a number of advantages to using UAVs. Many UAVs can be controlled by a single operator, which reduces personnel costs; they are very fast and can patrol large regions; and they have wider regions of visibility than conventional surveillance methods, which increases the probability of detecting intruders. This thesis formulates mathematical models designed to find the best way to utilize a given fleet of UAVs by deciding their routes, altitudes, and speeds in order to maximize the probability of detecting intruders trying to trespass a given border. These models will enable decision makers to effectively acquire and employ a UAV fleet for border surveillance. 2014-08-13T20:18:00Z 2014-08-13T20:18:00Z 2014-06 Thesis http://hdl.handle.net/10945/42729 This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted. Monterey, California: Naval Postgraduate School
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description Approved for public release; distribution is unlimited === Border surveillance is an important concern for most nations wanting to detect and intercept intruders that are trying to trespass a border. These intruders can include terrorists, drug traffickers, smugglers, illegal immigrants, and others who represent a threat to national interests. Unmanned aerial vehicles (UAVs) allow for modernization and improvement of border surveillance. There are a number of advantages to using UAVs. Many UAVs can be controlled by a single operator, which reduces personnel costs; they are very fast and can patrol large regions; and they have wider regions of visibility than conventional surveillance methods, which increases the probability of detecting intruders. This thesis formulates mathematical models designed to find the best way to utilize a given fleet of UAVs by deciding their routes, altitudes, and speeds in order to maximize the probability of detecting intruders trying to trespass a given border. These models will enable decision makers to effectively acquire and employ a UAV fleet for border surveillance.
author2 Craparo, Emily M.
author_facet Craparo, Emily M.
Sözen, Volkan
author Sözen, Volkan
spellingShingle Sözen, Volkan
Optimal deployment of unmanned aerial vehicles for border surveillance
author_sort Sözen, Volkan
title Optimal deployment of unmanned aerial vehicles for border surveillance
title_short Optimal deployment of unmanned aerial vehicles for border surveillance
title_full Optimal deployment of unmanned aerial vehicles for border surveillance
title_fullStr Optimal deployment of unmanned aerial vehicles for border surveillance
title_full_unstemmed Optimal deployment of unmanned aerial vehicles for border surveillance
title_sort optimal deployment of unmanned aerial vehicles for border surveillance
publisher Monterey, California: Naval Postgraduate School
publishDate 2014
url http://hdl.handle.net/10945/42729
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