ESTIMATING LINEAR REGRESSION PARAMETERS IN THE PRESENCE OF NON-WHITE GAUSSIAN NOISE WITH UNKNOWN COVARIANCE PARAMETERS

Abstract

A number of problems that arise in radar and sonar applications can be regarded as parameter estimation problems, in which the desired signal, f(t, alpha), is imbedded in non-white, Gaussian noise. It is desired to estimate the unknown, nonrandom parameter vector, alpha, from observations (continuous or sampled) of the received noisy signal over a finite time interval (0,T). Here f(t,alpha) is a known nonstochastic function, and we shall consider the case when f(t,alpha) is linear in alpha. In this case, alpha is referred to as a linear regression vector. We shall investigate the variance of the Least-Square (LS) estimator and of the so-called Generalized-Least-Squares (GLS) estimator for alpha. Both are unbiased estimators for alpha.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1968
Accession Number
AD0682968

Entities

People

  • Gene B. Goldstein

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Computational Science
  • Control Systems
  • Covariance
  • Data Science
  • Estimators
  • Gaussian Noise
  • Information Science
  • Intervals
  • Iterations
  • Monte Carlo Method
  • New York
  • Plastic Explosives
  • Simulations
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Statistical inference.