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Development of Code for a Physical Optics Radar Cross Section Prediction and Analysis Application

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Development of Code for a Physical Optics Radar Cross Section Prediction and Analysis Application

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    Available in PDF Format | Development of Code for a Physical Optics Radar Cross Section Prediction and Analysis Application.pdf | Unknown
    Filippos Chatzigeorgiadis
This is a NAVAL POSTGRADUATE SCHOOL MONTEREY CA report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A021724. The abstract provided by the Pentagon follows: The significance of the Radar Cross Section (RCS) in the outcome of military engagements makes its prediction an important problem in modern Electronic Warfare. The POFACETS program, previously developed at the Naval Postgraduate School (NPS), uses the Physical Optics method to predict the RCS of complex targets, which are modeled with the use of triangular facets. The program has minimum computer resource requirements and provides convenient run- times. This thesis up-graded, enhanced and expanded the functionalities and capabilities of the POFACETS program. The new functionalities were implemented by upgrading the Graphical User Interface and model database, allowing the creation of models with an unlimited number of facets, providing capabilities for the automatic creation of models with standard geometric shapes, allowing the combination of existing target models, providing capabilities for sharing target models with commercial CAD programs, and creating new display formats for RCS results. The new computational capabilities include the development of a user-updateable database of materials and coating that can be applied to models in one or multiple layers, and the computation of their effects on the models' RCS. Also implemented are the computations of the ground's effect on the RCS, and the exploitation of symmetry planes in models, in order to decrease run-time for RCS prediction.  
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