Detection in a Non-Gaussian Environment.

Abstract

Techniques for the detection of a weak signal in non-Gaussian, ill-defined noise are considered. Statistical characterizations used are moments, tail measures related to quantiles, and a measure related to the score function. For multivariate densities, the characterization is by means of a nonlinear transformation. Initial results seem to indicate that assuming a particular family of probability densities does not necessarily result in a significant degradation in performance when the observations actually come from a density outside the assumed family. More important to performance are accurate estimates of the moments, tail measures, or other parameters which are used to specify the detector. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1982
Accession Number
ADA120721

Entities

People

  • John B. Thomas
  • Stuart C. Schwartz

Organizations

  • Princeton University

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Data Science
  • Detectors
  • Electrical Engineering
  • Engineering
  • Environment
  • False Alarms
  • Gaussian Noise
  • Information Science
  • Information Theory
  • Military Research
  • Probability
  • Random Variables
  • Signal Processing
  • Simulations
  • Standards
  • Statistics
  • Test And Evaluation

Readers

  • Statistical inference.