On the Problem of Optimal Signal Detection in Discrete-Time, Correlated, Non-Gaussian Noise

Abstract

Recent results of the detection of signals in discrete-time correlated, non-Gaussian noise in which the univariate statistics and a general covariance structure of the noise are known have been obtained. the results are predicted on the assumption that a solution to the signal detection problem based on knowledge of univariate statistics and a convariance structure is 'reasonable,' even though it is known that in general a non-Gaussian noise process is not completely specified by such information. to examine this issue of 'reasonableness,' we present two general non-Gaussian noise models that are equivalent in these assumed attributes and yet lead to fundamentally different detection structures. This difference in the detection structures indicate the signal detection problem is not adequately formulated without additional knowledge of the structure of the non-Gaussian noise process. We further present a specific radar example to quantify the difference in the detection structures.

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Document Details

Document Type
Technical Report
Publication Date
Feb 23, 1989
Accession Number
ADA205859

Entities

People

  • B. H. Cantrell
  • K. J. Sangston

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Clutter
  • Covariance
  • Data Science
  • Detection
  • Detectors
  • Distribution Functions
  • Gaussian Noise
  • Gaussian Processes
  • Information Science
  • Integral Equations
  • Matched Filters
  • Radar Clutter
  • Radar Signals
  • Random Variables
  • Signal Detection
  • Statistics
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radar Systems Engineering.
  • Regression Analysis.