Accession Number : ADA307246

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

Corporate Author : ROME LAB ROME NY

Personal Author(s) : Michels, James H.

PDF Url : ADA307246

Report Date : JAN 1996

Pagination or Media Count : 26

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.

Descriptors :   *RANDOM VARIABLES, *COVARIANCE, MATHEMATICAL MODELS, SIGNAL PROCESSING, WEIBULL DENSITY FUNCTIONS, TIME DEPENDENCE, PARAMETERS, MAXIMUM LIKELIHOOD ESTIMATION, MATRICES(MATHEMATICS), ANALYSIS OF VARIANCE, WHITE NOISE, GAUSSIAN NOISE, MONTE CARLO METHOD, HIGH RESOLUTION, RADAR CLUTTER, RADAR SIGNALS, STATISTICAL PROCESSES, NORMALIZING(STATISTICS), APPLIED MATHEMATICS, INVARIANCE, PHASED ARRAYS, DISTRIBUTION FUNCTIONS, NOISE(RADAR), ANALYTIC FUNCTIONS.

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE