Innovations Based Detection Algorithm for Correlated Non-Gaussian Processes Using Multichannel Data,

Abstract

This report addresses the problem of multichannel signal detection in additive, correlated, non-Gaussian noise using the innovations approach. While this problem has been addressed extensively for the case of additive Gaussian noise, the corresponding problem for the non-Gaussian case has received limited attention. This is due to the fact that there is no unique specification for the joint probability density function (PDF) of N correlated non-Gaussian random variables. We overcome this problem by using the theory of spherically invariant random processes (SIRP) and derive the innovations based detectors. It is found that the optimal estimators for obtaining the innovations processes are linear and that the resulting detector is canonical for the class of PDFs arising from SIRPs. Detection algorithms, Multichannel data. Non-Gaussian clutter, Statistics.

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1993
Accession Number
ADA282054

Entities

People

  • James H. Michels
  • Muralidhar Rangaswamy

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Detection
  • Detectors
  • Estimators
  • Gaussian Noise
  • Gaussian Processes
  • Information Science
  • Multichannel
  • Optimal Estimators
  • Probability
  • Probability Density Functions
  • Random Variables
  • Signal Detection
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Engineering

Readers

  • Statistical inference.