LINE OF SIGHT ANALYSIS USING A FEEDFORWARD NEURAL NETWORK AND ONE-METER RESOLUTION DIGITAL ELEVATION MODEL (DEM) MAP DATA
Grant, John M.
Gordis, Joshua H.
Wade, Brian M.
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The 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.
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 States
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