ERROR PROBABILITY IN ENERGY DETECTION OF GAUSSIAN SIGNALS THROUGH RC FILTERS.

Abstract

A receiver structure model consisting of a filter followed by a squarer and integrator is examined. The signal and noise are both considered to be sample functions of a white gaussian process. The RC low-pass filter equivalent of an RLC filter is used in this analysis. The covariance function of this filter, when used as the kernel of the Wiener-Hopf integral equation, is used to derive the eigenvalues of the filter. An expression for the error probability of the receiver model in terms of these eigenvalues is given. The computation of this error probability, performed on the CDC 1604 digital computer, used approximately 1500 eigenvalues. This probability of error is computed for low values of the BT product, where B is the filter bandwidth and T the duration of the interval of observation A curve is presented, showing computed probabilities of error as a function of the signal-to-noise power ratio with a value of 0.159 for the parameter BT. A comparison is made with the previously used chi-squared approximation, and it is pointed out that the number of degrees of freedom used in the latter should not be the commonly accepted value of 2BT.

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1966
Accession Number
AD0487352

Entities

People

  • Alfred S. Kobos
  • Charles N. Meyer

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computers
  • Data Science
  • Digital Computers
  • Eigenvalues
  • Equations
  • Filters
  • Gaussian Processes
  • Information Science
  • Integral Equations
  • Low Pass Filters
  • Probability

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Statistical inference.