Detection of Signals in Non-Gaussian Correlated Noise Derived from Cauchy Processes

Abstract

A new processor for detecting a radar target in correlated, non- Gaussian noise is obtained. When this processor and a matched filter are excited with this noise, performance is improved over that of the matched filter alone. The processor is obtained by developing an approximate, bivariate, probability density for the noise and constructing a Neyman Pearson test and then using an approximation to the likelihood ratio obtained from the Neyman Pearson test. The bivariate density was constructed from an underlying Cauchy process and matches the true bivariate density only in the marginals and first two moments. Keywords: Detection; Radar signal processing, Radar detection.

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Document Details

Document Type
Technical Report
Publication Date
Oct 14, 1987
Accession Number
ADA187488

Entities

People

  • Ben Cantrell

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Data Science
  • Detection
  • Detectors
  • Distribution Functions
  • False Alarms
  • Filters
  • Gaussian Noise
  • Information Science
  • Integrals
  • Matched Filters
  • Mathematics
  • Military Research
  • Noise
  • Probability
  • Random Variables
  • Sampling
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Radar Systems Engineering.
  • Statistical inference.