Adaptive Demodulation of Digital Modulated Signals.

Abstract

The estimation of the frequency spectrum of signals is of widespread importance in such diverse areas as seismic signal processing, vibration measurements, biomedical signal processing, speech transmission, etc. In communication systems, the estimation of the spectrum of a signal is of importance for both demodulation of angle modulated signals and in frequency synchronization. A commonly used technique for spectral estimation in stochastic signals is to identify the parameters of an autoregressive model of the signal and then determine the spectrum in terms of these parameters. Instead of identifying the autoregressive parameters, alternative parameters may be formulated which are preferable from an identification viewpoint. In this investigation, one such set of parameters is determined on the basis of sensitivity studies. The parameters are obtained from a nonlinear transformation of the autoregressive parameters. A maximum likelihood estimation scheme is used to obtain appropriate sequential algorithms for identification of these parameters. Several simulation examples are presented to illustrate the applicability of the algorithms for spectral estimation in a stationary stochastic process.

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 1978
Accession Number
ADA062463

Entities

People

  • M. D. Srinath

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Communication Systems
  • Demodulation
  • Electrical Engineering
  • Frequency
  • Frequency Agility
  • Frequency Shift
  • Identification
  • Maximum Likelihood Estimation
  • Plastic Explosives
  • Signal Processing
  • Spectra
  • Speech Transmission
  • Spread Spectrum
  • Stationary Processes
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radio communications and signal processing.

Technology Areas

  • Biotechnology