Multipixel, Multidimensional Laser Radar System Performance.

Abstract

Laser radar analyses up to now generally fall into two categories. Single pixel analyses exist which cover receiver design, target statistics, atmospheric effects, and the resulting statistical performance characterizations like probabilities of detection (PD) and false alarm (PF). Multipixel analyses exist which use various ad HOC target recognition, identification, and feature extraction schemes based on intuitive insight or analogies to human perception. The limitation of these multipixel processors is it is difficult to quantitatively predict processor performance as a function of various system parameters and the resolving power in the measurement dimensions like range, Doppler shift, and angle. The single pixel performance equations can give quantitative answers to these questions, but only for single pixel measurements. This thesis bridges these two approaches by proposing physically realistic target, background, and radar models which allow us to incorporate the statistics for the single pixel measurements into multipixel probability density functions and derive quais-optimal generalized likelihood ratio processors from the densities.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA172841

Entities

People

  • Martin B. Mark

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Detection
  • Detectors
  • Doppler Effect
  • Electrical Engineering
  • Engineering
  • Image Processing
  • Laser Radar
  • Measurement
  • Optics
  • Radar
  • Random Variables
  • Reflectors
  • Signal Processing
  • Stochastic Processes
  • Surface Acoustic Waves

Readers

  • Distributed Systems and Data Platform Development
  • Radar Systems Engineering.
  • Statistical inference.

Technology Areas

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