THE ADAPTIVE DETECTION AND ESTIMATION OF NEARLY PERIODIC SIGNALS

Abstract

The report proposes a design of an adaptive receiver for the detection and estimation of nearly periodic signals in additive Gaussian noise. A nearly periodic signal is defined to be a sample function of a Gaussian random process which can be divided into equal length intervals, called periods, in such a manner that the correlation between periods decreases exponentially with their separation. The receiver computes a low signal-to-noise ratio conditional likelihood ratio from which the observer must make decisions. The likelihood ratio is conditional because the receiver estimates any unknown parameters necessary for the computation of the true likelihood ratio. Thus the receiver can only compute a likelihood ratio conditioned upon these estimates being the true values of the unknown parameters. The receiver consists of pre- emphasis filters followed by a comb filter, energy detector, and weighted summation. A theoretical evaluation of the receiver, in terms of ROC curves, is made for the special case of nearly periodic signals with statistically independent equal-strength harmonics in white noise of known power.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1969
Accession Number
AD0697003

Entities

People

  • Thomas G. Kincaid

Organizations

  • General Electric

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Comb Filters
  • Computations
  • Detection
  • Detectors
  • False Alarms
  • Filters
  • Filtration
  • Frequency
  • Gaussian Noise
  • Harmonics
  • Noise
  • Probability
  • Probability Density Functions
  • Random Variables
  • Warning Systems
  • White Noise

Readers

  • Approximation Theory.
  • Radio communications and signal processing.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference