Feasibility Study for the Mini-Frequency Agile Laser (MFAL) LIDAR System

Abstract

Direct and coherent detection LIDAR systems were analyzed for DISC and DIAL detection of stack and pollutant chemicals. Tradeoffs included output energies of 60 and 120 ml, powers of 2 and 10 W, and several values of telescope diameter, atmospheric attenuation, and backscatter coefficient. Longer ranges were found with coherent systems. Extrapolation from field data showed that direct detection of stack emissions could he effective up to 5 km. Based on prior laser development and field experience, a conceptual design for a man-portable, direct detection sensor gave a total weight of 33 lb with a length of 15 in., height of 12 in., and width of 6 in. Development of the conceptual design would require low risk application of standard engineering practices. Algorithms were formulated for application to the multi-wavelength data typical of a MiniFAL sensor to circumvent the limitations of the conventional ratio method using two closely spaced wavelengths. The algorithms used the likelihood ratio test methodology of multivariate statistical inference theory and were successfully applied to synthetic and field data. Wavelengths for most of the selected chemicals were identified; and by using wavelength shifting in crystals through nonlinear effects, all remaining chemicals of interest could be detected.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA399950

Entities

People

  • David B. Cohn
  • David S. Fink
  • John H. Wang
  • Russell E. Warren

Organizations

  • Hughes Aircraft Company

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Atmospheric Attenuation
  • Attenuation
  • Cameras
  • Composite Materials
  • Databases
  • Detection
  • Detectors
  • Engineering
  • Information Science
  • Laser Beams
  • Lasers
  • Measurement
  • Scattering
  • Standards
  • Statistical Algorithms
  • Statistical Inference

Readers

  • Environmental Engineering.
  • Optical Physics and Photonics.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Directed Energy
  • Space
  • Space - Space Objects