Sampling Requirements and Aliasing for Higher-Order Correlations

Abstract

While sampling at a Nyquist frequency equal to the highest frequency present in the data (critical sampling) is sufficient to prevent aliasing in both the data and the autocorrelation of a bandlimited energy signal, the sampling requirements for the avoidance of aliasing in higher-order correlations and spectra are not the same. Also, there is a difference in aliasing effects depending on whether one samples the original continuous-time signal and calculates the autocorrelation or one samples the continuous-time autocorrelation. This distinction between sampling procedures must be made for correlations of higher order, as well, for which not only the type of aliasing but also the sampling requirements to prevent aliasing differ. In particular, if one samples the continuous-time autobicorrelation or autotricorrelation, critical sampling is sufficient to prevent aliasing. In practice, however, it is not usually the continuous-time autobiocorrelation or autotricorrelation that is sampled. Generally, it is the original continuous-time signal that is sampled and used to calculate the discrete-time autobicorrelation or autotricorrelation. Transients, Distributed sensors, Coherence, Detection, Classification

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1993
Accession Number
ADA273027

Entities

People

  • George E. Loup
  • Juliette W. Loup
  • Lisa A. Pflug

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Blood Coagulation Factors
  • Detection
  • Detectors
  • Digital Signal Processing
  • Frequency
  • Frequency Domain
  • Military Research
  • New York
  • Noise
  • Power Spectra
  • Sensor Networks
  • Sequences
  • Signal Processing
  • Spectra
  • Three Dimensional
  • Time Domain
  • Time Signals

Fields of Study

  • Engineering

Readers

  • Approximation Theory.