Covariance Matrix Estimator Performance In Non-Gaussian Spherically Invariant Random Processes. Revision,

Abstract

This report describes the performance of the covariance matrix estimator in non-Gaussian spherically invariant random processes (SIRP). Analytic expressions are derived for the variance of the estimator. Specific consideration is given to the special cases of Weibull and K-distributed processes as a function of the shape parameter. Validation is achieved via Monte-Carlo simulation. The expressions reveal the increase in the estimator variance for non-Gaussian SIRP's as well as the sample support size required to reduce the variance to that of the Gaussian case.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1996
Accession Number
ADA307246

Entities

People

  • James H. Michels

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Covariance
  • Data Science
  • Estimators
  • Gaussian Noise
  • Gaussian Processes
  • Information Science
  • Monte Carlo Method
  • New York
  • Noise
  • Phased Arrays
  • Probability Density Functions
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Validation

Readers

  • Statistical inference.