Threshold Detection in Narrowband Non-Gaussian Noise.

Abstract

The Middleton Class A narrowband non-Gaussian noise model is examined. It is shown that this noise model (which is known to fit closely a variety of non-Gaussian noises) can itself be closely approximated by a computationally much simpler noise model. It is then shown by numerical examples that, for the problem of locally optimum detection, the simplest form of this approximation yields nearly optimal (asymptotic) performance. The performance of other simple suboptimal threshold detectors in Class A noise is also examined. Finally, a useful relationship between the Class A model and the epsilon-mixture model is developed. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1983
Accession Number
ADA127319

Entities

People

  • Kenneth S. Vastola

Organizations

  • Princeton University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bandwidth
  • Broadband
  • Data Science
  • Detection
  • Detectors
  • Gaussian Noise
  • Information Science
  • Logistics Management
  • Mathematics
  • Military Research
  • Narrowband
  • Probability
  • Probability Density Functions
  • Regions
  • Statistics
  • Test And Evaluation

Readers

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  • Statistical inference.