Optimal Estimation of Clock Values and Trends From Finite Data

Abstract

We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation; these principles lead to a set of linear equations for the regression coefficients and some auxiliary parameters. The mean square errors of the estimators are easily calculated. The method can be used to check the results of other methods and to find good suboptimal estimators based on a small subset of the available data.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA483845

Entities

People

  • Charles Greenhall

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Covariance
  • Equations
  • Estimators
  • Jet Propulsion
  • Kalman Filters
  • Optimal Estimators
  • Random Variables
  • Random Walk
  • Stationary
  • Stationary Processes
  • Statistical Algorithms
  • Statistical Analysis
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Statistical inference.