Log Spectral Estimation for Stationary and Nonstationary Processes

Abstract

This research is concerned with two log spectral estimators in the context of both stationary and nonstationary signals. They differ because in one smoothing is realized before the logarithmic transformation, while the other is smoothed in the logarithmic domain. It is shown that for stationary signals the two estimators are similar, differing in expected value by only a universal constant. The first estimator, however, is smoother. For nonstationary signals, the estimators are biased by different amounts dependent upon the nonstationarity. The difference between the estimators is shown to be a sensitive test for nonstationarity. The estimators are used in the analysis and implementation of two solutions to the problem of blind deconvolution. It is found that the methods are equivalent for stationary signals, but differ markedly for nonstationary signals in the presence of stationary background noise. Recommendations are made for the practical digital implementation of the log spectral estimators.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1976
Accession Number
ADA038697

Entities

People

  • Robert Bergstrom Ingebretsen

Organizations

  • University of Utah

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computer Science
  • Data Mining
  • Data Science
  • Department Of Veterans Affairs
  • Gaussian Processes
  • Information Processing
  • Information Science
  • Magnetic Disks
  • Mathematical Filters
  • Network Science
  • Normal Distribution
  • Probability Density Functions
  • Random Variables
  • Signal Processing
  • Stationary Processes
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Engineering

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Image Processing and Computer Vision.
  • Regression Analysis.