Show simple item record

dc.contributor.advisorGordis, Joshua H.
dc.contributor.advisorWade, Brian M.
dc.contributor.authorGrant, John M.
dc.dateSep-20
dc.date.accessioned2020-11-18T00:22:58Z
dc.date.available2020-11-18T00:22:58Z
dc.date.issued2020-09
dc.identifier.urihttp://hdl.handle.net/10945/66077
dc.description.abstractThe importance of maximizing one's Line of Sight (LOS) while minimizing enemy LOS is of critical importance in war. LOS between an observer and a target exists if a straight-line vector between the observer and target is not intersected by terrain. Many sensors and kinetic or non-kinetic weapons and enablers require intervisibility between the shooter and target for employment. A means to analyze a terrain map and determine one's LOS would aid route planning onboard aircraft to minimize exposure to ground-based sensors. Furthermore, most LOS programs are computationally expensive to run at scale, making any such analysis on board small aircraft generally unavailable to analyze a large terrain set or to analyze many LOS vectors between formations of sensors/shooters and targets. An LOS machine-learning estimate may solve this problem by reducing computational time, allowing a large number of LOS calculations to be performed with relatively small computation resources found on a laptop. Rapid and computationally efficient LOS calculations would aid warfighters in either maximizing their LOS (such as for an anti-aircraft missile placement) or minimizing their LOS (such as for a vulnerable helicopter needing to hide from potential enemies). The goal of this work is to determine whether such a machine-learning model can reduce the computation time for a large set of LOS calculations as compared to traditional LOS calculation methods with minimal loss in accuracy.en_US
dc.description.urihttp://archive.org/details/lineofsightanaly1094566077
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis 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 Statesen_US
dc.titleLINE OF SIGHT ANALYSIS USING A FEEDFORWARD NEURAL NETWORK AND ONE-METER RESOLUTION DIGITAL ELEVATION MODEL (DEM) MAP DATAen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical and Aerospace Engineering (MAE)
dc.subject.authorline of sighten_US
dc.subject.authorneural networken_US
dc.subject.authormachine learningen_US
dc.subject.authorterrain analysisen_US
dc.subject.authorpath planningen_US
dc.subject.authorPythonen_US
dc.subject.authorKerasen_US
dc.subject.authorDEMen_US
dc.description.serviceCivilian, Department of the Navyen_US
etd.thesisdegree.nameMaster of Science in Engineering Science (Aerospace Engineering)en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineEngineering Science (Aerospace Engineering)en_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid34715
dc.description.distributionstatementApproved for public release. distribution is unlimiteden_US
dc.identifier.curriculumcode608, Aerospace Engineering (DL)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record