The Theory of Detection in Incompletely Characterized Non-Gaussian Noise

Abstract

The problem of detecting a signal known except for amplitude in non- Gaussian noise is addressed. The noise samples are assumed to be independent and identically distributed with a probability density function known except for a few parameters. Using a generalized likelihood ratio test it is proven that for a symmetric noise probability density function the detection performance is asymptotically equivalent to that obtained for a detector designed with a priori knowledge of the noise parameters. A computationally more efficient but equivalent test is proposed and a computer simulation performed to illustrate the theory. Keywords: Electrical engineering.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1985
Accession Number
ADA162607

Entities

People

  • Steven Kay

Organizations

  • University of Rhode Island

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Computer Simulations
  • Contracts
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Gaussian Noise
  • Matched Filters
  • Military Research
  • Probability
  • Probability Density Functions
  • Random Variables
  • Rhode Island
  • Simulations
  • Statistics
  • Test And Evaluation
  • Universities

Fields of Study

  • Engineering

Readers

  • Statistical inference.