FLIR Performance Modeling and its Dependence upon Climatology and Meteorology

Abstract

In this presentation I shall address primarily the meteorologists and climatologists in this rather heterogeneous group and assume that the FLIR and modeling experts are here only to keep me honest. Some of the questions that I hope to answer are: What is a "FLIR"? What is FLIR performance modeling? Why is FLIR modeling important? What are the ingredients that go into a FLIR mathematical modeling exercise? What deficiencies (particularly in the fields of meteorology and climatology)exist in the available information needed to satisfy such a model? How can these deficiencies be relieved? FLIR is an acronym (not a very good one) for "Forward Looking InfraRed" which was first coined about 11 years ago to distinguish this relatively new class of airborne equipment from an earlier generation of infrared imaging devices that look primarily in a downward direction. A FLIR functions as a sort of television camera that can see in absolute darkness. It is usually mounted on gimbals in the forward part of an airplane in such a manner that the operator can aim it in any desired direction - typically forward. A FLIR performance model may be defined ideally, as a mathematical description of a FLIR and of all the relevant environmental, operational, and human factors that affect its performance.

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Document Details

Document Type
Technical Report
Publication Date
Aug 07, 1975
Accession Number
AD1037652

Entities

People

  • Paul M. Moser

Organizations

  • Naval Air Warfare Center Warminster

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Altitude
  • Atlantic Ocean
  • Carbon Dioxide
  • Collision Avoidance
  • Detectors
  • Electro-Optics
  • Identification
  • Infrared Radiation
  • Mathematical Models
  • Meteorology
  • Models
  • Navy
  • Radiation
  • Scattering
  • Sea Level
  • Water Vapor

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.
  • Systems Analysis and Design