Estimating the Instabilities of N Clocks by Means of Comparison Measurements

Abstract

The estimation of individual instabilities of N clocks, compared by measuring the differences of their readings, is here considered without assuming a priori any hypotheses on their uncorrelation. Instabilities of the N clocks are described by a complete (non-diagonal) NxN covariance matrix R. Only differences of clock readings are available in order to estimate R. Statistical processing of these data allows one to calculate the (N-1)x(N-1) covariance matrix S of the differences relative to the N-th(reference) clock. By analyzing the relationships tying R and S, several pieces of information can be inferred and, in particular, the conditions for the validity of the uncorrelation hypothesis are established. The estimation of R from S is not unique: in any case R must be positive definite. A theorem states that R is positive definite if and only if its determinant is positive. Nevertheless infinitely many acceptable choices of R still fulfill the condition of positive definiteness. This paper shows that, by increasing the number N of compared clocks, the amount of arbitrariness in estimating R is reduced. The analysis of some experimental data illustrates the capability of the method.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1992
Accession Number
ADA501910

Entities

People

  • Amedeo Premoli
  • Patrizia Tavella

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Boundaries
  • Clocks
  • Covariance
  • Ellipsoids
  • Equations
  • Experimental Data
  • Frequency Standards
  • Information Operations
  • Instability
  • Linear Systems
  • Low Noise
  • Mathematical Models
  • Measurement
  • Noise
  • Standards
  • Statistical Samples
  • Three Dimensional

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Positioning, Navigation, and Timing (PNT) Technology.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference