PARAMETER ESTIMATION THEORY AND SOME APPLICATIONS OF THE THEORY TO RADAR MEASUREMENTS

Abstract

The general theory of parameter estimation is developed using the inverse probability approach. Where the measurements are perturbed by additive Gaussian noise and when the received information is sufficient to determine the parameters of interest rather accurately, it is shown that an optimum method of processing redundant data based on the maximum likelihood approach reduces approximately to the solution of k nonlinear equations in the k unknown parameters. An expression is derived for the resulting error moment matrix of the parameters. It is shown that this same moment matrix for a minimum variance estimate is obtained by using results derived by Cramer. The theory is illustrated by applying it to several radar measurement problems of interest.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1960
Accession Number
AD0287562

Entities

People

  • R. Manasse

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Angle Of Arrival
  • Carrier Frequencies
  • Equations
  • Errors
  • Frequency
  • Gaussian Noise
  • Government Procurement
  • Intervals
  • Measurement
  • Narrowband
  • Normal Distribution
  • Probability
  • Radar Pulses
  • Radial Velocity
  • Time Intervals

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.