Statistical Studies of the Monopulse Ratio.

Abstract

The complex sum and difference phasers in a monopulse system provide four real quantities that are subject to random errors because of noise. Consequently, the monopulse ratio (MR), which depends on these four measured quantities, is a stochastic variable. The joint distribution of the real and imaginary parts of the MR is obtained for several cases under the assumption that the primary variables have a four-variate Gaussian distribution. The marginal distribution for the real part of the MR, or its mean, is presented in a few simple cases. Three noise models are discussed. The first assumes the noise to be uncorrelated and isotropic. The second allows real-to-real and imaginary-to-imaginary correlation between the sum and difference phasers. The third treats a general noise covariance matrix. The variance of the MR is known to be infinite. However, density distributions with finite variance are obtained if a threshold is placed on the magnitude of the sum phaser, discarding cases lying below the threshold. The resulting variance is developed for two simple cases where the primary mean values are zero. The dependence of the joint MR density on threshold is studied. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1994
Accession Number
ADA292294

Entities

People

  • Gordon W. Groves
  • W.D. Blair

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Bessel Functions
  • Data Science
  • Department Of Defense
  • Engineering
  • Equations
  • Errors
  • Information Science
  • Measurement
  • Military Research
  • Monopulse Radar
  • Monte Carlo Method
  • Numbers
  • Radar
  • Random Variables
  • Simulations
  • Standards

Fields of Study

  • Physics

Readers

  • Microwave Engineering.
  • Radar Systems Engineering.
  • Statistical inference.